Pandas Profiling Report

Dataset statistics

Number of variables	63
Number of observations	2261
Missing cells	6647
Missing cells (%)	4.7%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	1.1 MiB
Average record size in memory	497.2 B

Variable types

Numeric	8
Categorical	52
Boolean	3

Alerts

`country` has a high cardinality: 105 distinct values	High cardinality
`currency_desc` has a high cardinality: 73 distinct values	High cardinality
`currency_symbol` has a high cardinality: 73 distinct values	High cardinality
`database_desire_next_year` has a high cardinality: 576 distinct values	High cardinality
`database_worked_with` has a high cardinality: 594 distinct values	High cardinality
`dev_type` has a high cardinality: 995 distinct values	High cardinality
`ethnicity` has a high cardinality: 51 distinct values	High cardinality
`job_factors` has a high cardinality: 180 distinct values	High cardinality
`language_desire_next_year` has a high cardinality: 1335 distinct values	High cardinality
`language_worked_with` has a high cardinality: 1271 distinct values	High cardinality
`misc_tech_desire_next_year` has a high cardinality: 783 distinct values	High cardinality
`misc_tech_worked_with` has a high cardinality: 578 distinct values	High cardinality
`new_collab_tools_desire_next_year` has a high cardinality: 402 distinct values	High cardinality
`new_collab_tools_worked_with` has a high cardinality: 406 distinct values	High cardinality
`new_job_hunt` has a high cardinality: 697 distinct values	High cardinality
`new_job_hunt_research` has a high cardinality: 62 distinct values	High cardinality
`new_purchase_research` has a high cardinality: 56 distinct values	High cardinality
`new_stuck` has a high cardinality: 215 distinct values	High cardinality
`platform_desire_next_year` has a high cardinality: 948 distinct values	High cardinality
`platform_worked_with` has a high cardinality: 897 distinct values	High cardinality
`webframe_desire_next_year` has a high cardinality: 435 distinct values	High cardinality
`webframe_worked_with` has a high cardinality: 502 distinct values	High cardinality
`age` is highly correlated with `years_code` and 1 other fields	High correlation
`years_code` is highly correlated with `age` and 1 other fields	High correlation
`years_code_pro` is highly correlated with `age` and 1 other fields	High correlation
`age` is highly correlated with `years_code` and 1 other fields	High correlation
`years_code` is highly correlated with `age` and 1 other fields	High correlation
`years_code_pro` is highly correlated with `age` and 1 other fields	High correlation
`age` is highly correlated with `years_code` and 1 other fields	High correlation
`years_code` is highly correlated with `age` and 1 other fields	High correlation
`years_code_pro` is highly correlated with `age` and 1 other fields	High correlation
`gender` is highly correlated with `trans`	High correlation
`so_part_freq` is highly correlated with `so_account`	High correlation
`currency_symbol` is highly correlated with `currency_desc`	High correlation
`trans` is highly correlated with `gender`	High correlation
`currency_desc` is highly correlated with `currency_symbol`	High correlation
`so_account` is highly correlated with `so_part_freq`	High correlation
`age` is highly correlated with `years_code` and 2 other fields	High correlation
`age_1st_code` is highly correlated with `age_first_code_cut`	High correlation
`age_first_code_cut` is highly correlated with `age_1st_code`	High correlation
`comp_freq` is highly correlated with `converted_comp` and 2 other fields	High correlation
`comp_total` is highly correlated with `currency_desc` and 1 other fields	High correlation
`converted_comp` is highly correlated with `comp_freq`	High correlation
`currency_desc` is highly correlated with `comp_freq` and 4 other fields	High correlation
`currency_symbol` is highly correlated with `comp_freq` and 4 other fields	High correlation
`employment` is highly correlated with `org_size`	High correlation
`ethnicity` is highly correlated with `currency_desc` and 4 other fields	High correlation
`gender` is highly correlated with `ethnicity` and 2 other fields	High correlation
`new_purchase_research` is highly correlated with `ethnicity` and 1 other fields	High correlation
`newso_sites` is highly correlated with `currency_desc` and 2 other fields	High correlation
`org_size` is highly correlated with `employment`	High correlation
`sexuality` is highly correlated with `ethnicity` and 2 other fields	High correlation
`so_account` is highly correlated with `so_comm`	High correlation
`so_comm` is highly correlated with `so_account` and 1 other fields	High correlation
`so_part_freq` is highly correlated with `so_comm`	High correlation
`trans` is highly correlated with `gender` and 1 other fields	High correlation
`years_code` is highly correlated with `age` and 2 other fields	High correlation
`years_code_pro` is highly correlated with `age` and 2 other fields	High correlation
`age_cat` is highly correlated with `age` and 2 other fields	High correlation
`database_desire_next_year` has 574 (25.4%) missing values	Missing
`database_worked_with` has 327 (14.5%) missing values	Missing
`ethnicity` has 169 (7.5%) missing values	Missing
`gender` has 33 (1.5%) missing values	Missing
`job_factors` has 28 (1.2%) missing values	Missing
`language_desire_next_year` has 101 (4.5%) missing values	Missing
`misc_tech_desire_next_year` has 307 (13.6%) missing values	Missing
`misc_tech_worked_with` has 297 (13.1%) missing values	Missing
`new_collab_tools_desire_next_year` has 238 (10.5%) missing values	Missing
`new_collab_tools_worked_with` has 69 (3.1%) missing values	Missing
`new_dev_ops_impt` has 78 (3.4%) missing values	Missing
`new_job_hunt` has 70 (3.1%) missing values	Missing
`new_job_hunt_research` has 112 (5.0%) missing values	Missing
`new_learn` has 38 (1.7%) missing values	Missing
`new_off_topic` has 106 (4.7%) missing values	Missing
`new_purchase_research` has 781 (34.5%) missing values	Missing
`new_stuck` has 29 (1.3%) missing values	Missing
`org_size` has 38 (1.7%) missing values	Missing
`platform_desire_next_year` has 196 (8.7%) missing values	Missing
`platform_worked_with` has 91 (4.0%) missing values	Missing
`purchase_what` has 208 (9.2%) missing values	Missing
`sexuality` has 207 (9.2%) missing values	Missing
`so_part_freq` has 377 (16.7%) missing values	Missing
`trans` has 62 (2.7%) missing values	Missing
`undergrad_major` has 110 (4.9%) missing values	Missing
`webframe_desire_next_year` has 925 (40.9%) missing values	Missing
`webframe_worked_with` has 809 (35.8%) missing values	Missing
`welcome_change` has 62 (2.7%) missing values	Missing
`work_week_hrs` has 42 (1.9%) missing values	Missing
`comp_total` is highly skewed (γ1 = 26.99760535)	Skewed
`respondent` has unique values	Unique

Reproduction

Analysis started	2022-08-24 11:10:25.531204
Analysis finished	2022-08-24 11:11:19.748954
Duration	54.22 seconds
Software version	pandas-profiling v3.2.0
Download configuration	config.json

respondent
Real number (ℝ_≥0)

UNIQUE

Distinct	2261
Distinct (%)	100.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	28919.82795

Minimum	36
Maximum	62882
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	36
5-th percentile	2279
Q1	14033
median	26119
Q3	46996
95-th percentile	58954
Maximum	62882
Range	62846
Interquartile range (IQR)	32963

Descriptive statistics

Standard deviation	18404.37771
Coefficient of variation (CV)	0.6363930569
Kurtosis	-1.186327816
Mean	28919.82795
Median Absolute Deviation (MAD)	15885
Skewness	0.228296954
Sum	65387731
Variance	338721119.1
Monotonicity	Strictly increasing

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
36	1	< 0.1%
35609	1	< 0.1%
35324	1	< 0.1%
35327	1	< 0.1%
35440	1	< 0.1%
35450	1	< 0.1%
35536	1	< 0.1%
35590	1	< 0.1%
35705	1	< 0.1%
35293	1	< 0.1%
Other values (2251)	2251	99.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
36	1	< 0.1%
47	1	< 0.1%
69	1	< 0.1%
125	1	< 0.1%
147	1	< 0.1%
152	1	< 0.1%
166	1	< 0.1%
170	1	< 0.1%
187	1	< 0.1%
196	1	< 0.1%

Value	Count	Frequency (%)
62882	1	< 0.1%
62867	1	< 0.1%
62837	1	< 0.1%
62835	1	< 0.1%
62812	1	< 0.1%
62745	1	< 0.1%
62705	1	< 0.1%
62617	1	< 0.1%
62616	1	< 0.1%
62608	1	< 0.1%

main_branch
Categorical

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

I am a developer by profession	1773
I am not primarily a developer, but I write code sometimes as part of my work	488

Length

Max length	77
Median length	30
Mean length	40.14418399
Min length	30

Characters and Unicode

Total characters	90766
Distinct characters	23
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	I am not primarily a developer, but I write code sometimes as part of my work
2nd row	I am a developer by profession
3rd row	I am a developer by profession
4th row	I am not primarily a developer, but I write code sometimes as part of my work
5th row	I am not primarily a developer, but I write code sometimes as part of my work

Common Values

Value	Count	Frequency (%)
I am a developer by profession	1773	78.4%
I am not primarily a developer, but I write code sometimes as part of my work	488	21.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
i	2749	14.9%
a	2261	12.3%
developer	2261	12.3%
am	2261	12.3%
by	1773	9.6%
profession	1773	9.6%
sometimes	488	2.6%
my	488	2.6%
of	488	2.6%
part	488	2.6%
Other values (7)	3416	18.5%

Most occurring characters

Value	Count	Frequency (%)
	16185	17.8%
e	10508	11.6%
o	8247	9.1%
r	6474	7.1%
a	5986	6.6%
p	5010	5.5%
s	5010	5.5%
m	4213	4.6%
i	3725	4.1%
I	2749	3.0%
Other values (13)	22659	25.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	71344	78.6%
Space Separator	16185	17.8%
Uppercase Letter	2749	3.0%
Other Punctuation	488	0.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	10508	14.7%
o	8247	11.6%
r	6474	9.1%
a	5986	8.4%
p	5010	7.0%
s	5010	7.0%
m	4213	5.9%
i	3725	5.2%
d	2749	3.9%
l	2749	3.9%
Other values (10)	16673	23.4%

Space Separator

Value	Count	Frequency (%)
	16185	100.0%

Uppercase Letter

Value	Count	Frequency (%)
I	2749	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	488	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	74093	81.6%
Common	16673	18.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	10508	14.2%
o	8247	11.1%
r	6474	8.7%
a	5986	8.1%
p	5010	6.8%
s	5010	6.8%
m	4213	5.7%
i	3725	5.0%
I	2749	3.7%
d	2749	3.7%
Other values (11)	19422	26.2%

Common

Value	Count	Frequency (%)
	16185	97.1%
,	488	2.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	90766	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	16185	17.8%
e	10508	11.6%
o	8247	9.1%
r	6474	7.1%
a	5986	6.6%
p	5010	5.5%
s	5010	5.5%
m	4213	4.6%
i	3725	4.1%
I	2749	3.0%
Other values (13)	22659	25.0%

hobbyist
Boolean

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	2.3 KiB

True	1833
False	428

Common Values
Category Frequency Plot

Value	Count	Frequency (%)
True	1833	81.1%
False	428	18.9%

age
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	53
Distinct (%)	2.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	31.90712074

Minimum	16
Maximum	85
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	16
5-th percentile	22
Q1	26
median	30
Q3	36
95-th percentile	48
Maximum	85
Range	69
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	8.225540178
Coefficient of variation (CV)	0.2577963786
Kurtosis	2.562049469
Mean	31.90712074
Median Absolute Deviation (MAD)	5
Skewness	1.343034715
Sum	72142
Variance	67.65951122
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
26	161	7.1%
30	160	7.1%
25	152	6.7%
28	146	6.5%
29	128	5.7%
27	118	5.2%
31	111	4.9%
24	109	4.8%
23	108	4.8%
32	98	4.3%
Other values (43)	970	42.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
16	1	< 0.1%
18	6	0.3%
19	6	0.3%
20	9	0.4%
21	32	1.4%
22	75	3.3%
23	108	4.8%
24	109	4.8%
25	152	6.7%
26	161	7.1%

Value	Count	Frequency (%)
85	1	< 0.1%
74	1	< 0.1%
73	1	< 0.1%
72	1	< 0.1%
66	1	< 0.1%
64	1	< 0.1%
63	6	0.3%
62	1	< 0.1%
61	2	0.1%
60	1	< 0.1%

age_1st_code
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	36
Distinct (%)	1.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	14.93321539

Minimum	5
Maximum	45
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	5
5-th percentile	8
Q1	12
median	15
Q3	18
95-th percentile	23
Maximum	45
Range	40
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	4.767279494
Coefficient of variation (CV)	0.3192399874
Kurtosis	2.159029838
Mean	14.93321539
Median Absolute Deviation (MAD)	3
Skewness	0.8216747217
Sum	33764
Variance	22.72695377
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=36)

Value	Count	Frequency (%)
14	230	10.2%
12	220	9.7%
15	206	9.1%
18	189	8.4%
16	183	8.1%
10	178	7.9%
13	148	6.5%
17	133	5.9%
19	120	5.3%
11	107	4.7%
Other values (26)	547	24.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
5	18	0.8%
6	27	1.2%
7	38	1.7%
8	84	3.7%
9	65	2.9%
10	178	7.9%
11	107	4.7%
12	220	9.7%
13	148	6.5%
14	230	10.2%

Value	Count	Frequency (%)
45	1	< 0.1%
42	1	< 0.1%
40	1	< 0.1%
38	1	< 0.1%
36	1	< 0.1%
35	1	< 0.1%
34	1	< 0.1%
33	4	0.2%
32	1	< 0.1%
31	2	0.1%

age_first_code_cut
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

adult	1376
child	885

Length

Max length	5
Median length	5
Mean length	5
Min length	5

Characters and Unicode

Total characters	11305
Distinct characters	8
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	adult
2nd row	child
3rd row	child
4th row	adult
5th row	adult

Common Values

Value	Count	Frequency (%)
adult	1376	60.9%
child	885	39.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
adult	1376	60.9%
child	885	39.1%

Most occurring characters

Value	Count	Frequency (%)
d	2261	20.0%
l	2261	20.0%
a	1376	12.2%
u	1376	12.2%
t	1376	12.2%
c	885	7.8%
h	885	7.8%
i	885	7.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	11305	100.0%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
d	2261	20.0%
l	2261	20.0%
a	1376	12.2%
u	1376	12.2%
t	1376	12.2%
c	885	7.8%
h	885	7.8%
i	885	7.8%

Most occurring scripts

Value	Count	Frequency (%)
Latin	11305	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
d	2261	20.0%
l	2261	20.0%
a	1376	12.2%
u	1376	12.2%
t	1376	12.2%
c	885	7.8%
h	885	7.8%
i	885	7.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	11305	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
d	2261	20.0%
l	2261	20.0%
a	1376	12.2%
u	1376	12.2%
t	1376	12.2%
c	885	7.8%
h	885	7.8%
i	885	7.8%

comp_freq
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

Yearly	1338
Monthly	861
Weekly	62

Length

Max length	7
Median length	6
Mean length	6.380804954
Min length	6

Characters and Unicode

Total characters	14427
Distinct characters	13
Distinct categories	2 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yearly
2nd row	Yearly
3rd row	Yearly
4th row	Monthly
5th row	Yearly

Common Values

Value	Count	Frequency (%)
Yearly	1338	59.2%
Monthly	861	38.1%
Weekly	62	2.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
yearly	1338	59.2%
monthly	861	38.1%
weekly	62	2.7%

Most occurring characters

Value	Count	Frequency (%)
l	2261	15.7%
y	2261	15.7%
e	1462	10.1%
Y	1338	9.3%
a	1338	9.3%
r	1338	9.3%
M	861	6.0%
o	861	6.0%
n	861	6.0%
t	861	6.0%
Other values (3)	985	6.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	12166	84.3%
Uppercase Letter	2261	15.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
l	2261	18.6%
y	2261	18.6%
e	1462	12.0%
a	1338	11.0%
r	1338	11.0%
o	861	7.1%
n	861	7.1%
t	861	7.1%
h	861	7.1%
k	62	0.5%

Uppercase Letter

Value	Count	Frequency (%)
Y	1338	59.2%
M	861	38.1%
W	62	2.7%

Most occurring scripts

Value	Count	Frequency (%)
Latin	14427	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
l	2261	15.7%
y	2261	15.7%
e	1462	10.1%
Y	1338	9.3%
a	1338	9.3%
r	1338	9.3%
M	861	6.0%
o	861	6.0%
n	861	6.0%
t	861	6.0%
Other values (3)	985	6.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	14427	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
l	2261	15.7%
y	2261	15.7%
e	1462	10.1%
Y	1338	9.3%
a	1338	9.3%
r	1338	9.3%
M	861	6.0%
o	861	6.0%
n	861	6.0%
t	861	6.0%
Other values (3)	985	6.8%

comp_total
Real number (ℝ_≥0)

HIGH CORRELATION
SKEWED

Distinct	555
Distinct (%)	24.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3238166.69

Minimum	0
Maximum	1900000000
Zeros	5
Zeros (%)	0.2%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	0
5-th percentile	1800
Q1	27010
median	70000
Q3	143000
95-th percentile	1200000
Maximum	1900000000
Range	1900000000
Interquartile range (IQR)	115990

Descriptive statistics

Standard deviation	55932374.69
Coefficient of variation (CV)	17.27285221
Kurtosis	803.4420516
Mean	3238166.69
Median Absolute Deviation (MAD)	54000
Skewness	26.99760535
Sum	7321494887
Variance	3.128430538 × 10¹⁵
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
60000	65	2.9%
50000	59	2.6%
100000	45	2.0%
120000	44	1.9%
90000	39	1.7%
150000	39	1.7%
200000	39	1.7%
110000	36	1.6%
70000	35	1.5%
55000	35	1.5%
Other values (545)	1825	80.7%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	5	0.2%
1	2	0.1%
6	1	< 0.1%
50	1	< 0.1%
100	1	< 0.1%
140	1	< 0.1%
188	1	< 0.1%
200	2	0.1%
250	1	< 0.1%
300	2	0.1%

Value	Count	Frequency (%)
1900000000	1	< 0.1%
1425000000	1	< 0.1%
900000000	1	< 0.1%
525000000	1	< 0.1%
440000000	1	< 0.1%
200000000	1	< 0.1%
180000000	1	< 0.1%
150000000	1	< 0.1%
120000000	2	0.1%
100000000	1	< 0.1%

converted_comp
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	1178
Distinct (%)	52.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	119574.7174

Minimum	0
Maximum	2000000
Zeros	9
Zeros (%)	0.4%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	0
5-th percentile	6360
Q1	27492
median	58373
Q3	104208
95-th percentile	325000
Maximum	2000000
Range	2000000
Interquartile range (IQR)	76716

Descriptive statistics

Standard deviation	265556.0729
Coefficient of variation (CV)	2.220837972
Kurtosis	32.81638818
Mean	119574.7174
Median Absolute Deviation (MAD)	35165
Skewness	5.481555966
Sum	270358436
Variance	7.052002786 × 10¹⁰
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
64859	29	1.3%
2000000	27	1.2%
54049	23	1.0%
110000	23	1.0%
200000	22	1.0%
120000	19	0.8%
75669	18	0.8%
130000	18	0.8%
1000000	18	0.8%
70264	17	0.8%
Other values (1168)	2047	90.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	9	0.4%
24	1	< 0.1%
25	1	< 0.1%
54	1	< 0.1%
94	1	< 0.1%
188	1	< 0.1%
698	1	< 0.1%
840	1	< 0.1%
876	1	< 0.1%
936	1	< 0.1%

Value	Count	Frequency (%)
2000000	27	1.2%
1920000	2	0.1%
1800000	1	< 0.1%
1560000	1	< 0.1%
1440000	1	< 0.1%
1356000	1	< 0.1%
1344000	1	< 0.1%
1260000	1	< 0.1%
1200000	1	< 0.1%
1080000	1	< 0.1%

country
Categorical

HIGH CARDINALITY

Distinct	105
Distinct (%)	4.6%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

United States	544
India	186
Germany	167
United Kingdom	157
France	81
Other values (100)	1126

Length

Max length	36
Median length	22
Mean length	9.232198142
Min length	4

Characters and Unicode

Total characters	20874
Distinct characters	54
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	22 ?
Unique (%)	1.0%

Sample

1st row	United Kingdom
2nd row	United Kingdom
3rd row	France
4th row	United States
5th row	Canada

Common Values

Value	Count	Frequency (%)
United States	544	24.1%
India	186	8.2%
Germany	167	7.4%
United Kingdom	157	6.9%
France	81	3.6%
Brazil	80	3.5%
Canada	75	3.3%
Spain	65	2.9%
Australia	55	2.4%
Netherlands	54	2.4%
Other values (95)	797	35.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
united	707	22.6%
states	544	17.4%
india	186	6.0%
germany	167	5.3%
kingdom	157	5.0%
france	81	2.6%
brazil	80	2.6%
canada	75	2.4%
spain	65	2.1%
australia	55	1.8%
Other values (113)	1007	32.2%

Most occurring characters

Value	Count	Frequency (%)
a	2347	11.2%
e	2213	10.6%
t	2157	10.3%
n	2005	9.6%
i	1763	8.4%
d	1427	6.8%
s	875	4.2%
	863	4.1%
r	841	4.0%
S	745	3.6%
Other values (44)	5638	27.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	16846	80.7%
Uppercase Letter	3130	15.0%
Space Separator	863	4.1%
Other Punctuation	23	0.1%
Open Punctuation	6	< 0.1%
Close Punctuation	6	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	2347	13.9%
e	2213	13.1%
t	2157	12.8%
n	2005	11.9%
i	1763	10.5%
d	1427	8.5%
s	875	5.2%
r	841	5.0%
l	534	3.2%
o	423	2.5%
Other values (15)	2261	13.4%

Uppercase Letter

Value	Count	Frequency (%)
S	745	23.8%
U	732	23.4%
I	292	9.3%
G	188	6.0%
K	175	5.6%
F	143	4.6%
C	140	4.5%
B	126	4.0%
A	118	3.8%
N	110	3.5%
Other values (13)	361	11.5%

Other Punctuation

Value	Count	Frequency (%)
.	21	91.3%
,	1	4.3%
'	1	4.3%

Space Separator

Value	Count	Frequency (%)
	863	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	6	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	6	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	19976	95.7%
Common	898	4.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	2347	11.7%
e	2213	11.1%
t	2157	10.8%
n	2005	10.0%
i	1763	8.8%
d	1427	7.1%
s	875	4.4%
r	841	4.2%
S	745	3.7%
U	732	3.7%
Other values (38)	4871	24.4%

Common

Value	Count	Frequency (%)
	863	96.1%
.	21	2.3%
(	6	0.7%
)	6	0.7%
,	1	0.1%
'	1	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	20873	> 99.9%
None	1	< 0.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	2347	11.2%
e	2213	10.6%
t	2157	10.3%
n	2005	9.6%
i	1763	8.4%
d	1427	6.8%
s	875	4.2%
	863	4.1%
r	841	4.0%
S	745	3.6%
Other values (43)	5637	27.0%

None

Value	Count	Frequency (%)
ô	1	100.0%

currency_desc
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	73
Distinct (%)	3.2%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

United States dollar	597
European Euro	557
Indian rupee	182
Pound sterling	158
Brazilian real	79
Other values (68)	688

Length

Max length	39
Median length	27
Mean length	15.29500221
Min length	9

Characters and Unicode

Total characters	34582
Distinct characters	50
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	13 ?
Unique (%)	0.6%

Sample

1st row	Pound sterling
2nd row	Pound sterling
3rd row	European Euro
4th row	United States dollar
5th row	Canadian dollar

Common Values

Value	Count	Frequency (%)
United States dollar	597	26.4%
European Euro	557	24.6%
Indian rupee	182	8.0%
Pound sterling	158	7.0%
Brazilian real	79	3.5%
Canadian dollar	75	3.3%
Australian dollar	55	2.4%
Russian ruble	43	1.9%
Swiss franc	40	1.8%
Swedish krona	37	1.6%
Other values (63)	438	19.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
dollar	754	14.5%
united	602	11.5%
states	597	11.4%
european	557	10.7%
euro	557	10.7%
rupee	219	4.2%
indian	182	3.5%
pound	160	3.1%
sterling	158	3.0%
brazilian	79	1.5%
Other values (122)	1352	25.9%

Most occurring characters

Value	Count	Frequency (%)
a	3437	9.9%
e	3001	8.7%
r	2965	8.6%
	2956	8.5%
n	2835	8.2%
o	2384	6.9%
l	2194	6.3%
t	2152	6.2%
i	1886	5.5%
d	1880	5.4%
Other values (40)	8892	25.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	28131	81.3%
Uppercase Letter	3495	10.1%
Space Separator	2956	8.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	3437	12.2%
e	3001	10.7%
r	2965	10.5%
n	2835	10.1%
o	2384	8.5%
l	2194	7.8%
t	2152	7.6%
i	1886	6.7%
d	1880	6.7%
u	1773	6.3%
Other values (15)	3624	12.9%

Uppercase Letter

Value	Count	Frequency (%)
E	1120	32.0%
S	710	20.3%
U	620	17.7%
I	228	6.5%
P	227	6.5%
C	132	3.8%
A	95	2.7%
B	94	2.7%
R	57	1.6%
N	51	1.5%
Other values (14)	161	4.6%

Space Separator

Value	Count	Frequency (%)
	2956	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	31626	91.5%
Common	2956	8.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	3437	10.9%
e	3001	9.5%
r	2965	9.4%
n	2835	9.0%
o	2384	7.5%
l	2194	6.9%
t	2152	6.8%
i	1886	6.0%
d	1880	5.9%
u	1773	5.6%
Other values (39)	7119	22.5%

Common

Value	Count	Frequency (%)
	2956	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	34582	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	3437	9.9%
e	3001	8.7%
r	2965	8.6%
	2956	8.5%
n	2835	8.2%
o	2384	6.9%
l	2194	6.3%
t	2152	6.2%
i	1886	5.5%
d	1880	5.4%
Other values (40)	8892	25.7%

currency_symbol
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION

Distinct	73
Distinct (%)	3.2%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

USD	597
EUR	557
INR	182
GBP	158
BRL	79
Other values (68)	688

Length

Max length	3
Median length	3
Mean length	3
Min length	3

Characters and Unicode

Total characters	6783
Distinct characters	26
Distinct categories	1 ?
Distinct scripts	1 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	13 ?
Unique (%)	0.6%

Sample

1st row	GBP
2nd row	GBP
3rd row	EUR
4th row	USD
5th row	CAD

Common Values

Value	Count	Frequency (%)
USD	597	26.4%
EUR	557	24.6%
INR	182	8.0%
GBP	158	7.0%
BRL	79	3.5%
CAD	75	3.3%
AUD	55	2.4%
RUB	43	1.9%
CHF	40	1.8%
SEK	37	1.6%
Other values (63)	438	19.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
usd	597	26.4%
eur	557	24.6%
inr	182	8.0%
gbp	158	7.0%
brl	79	3.5%
cad	75	3.3%
aud	55	2.4%
rub	43	1.9%
chf	40	1.8%
sek	37	1.6%
Other values (63)	438	19.4%

Most occurring characters

Value	Count	Frequency (%)
U	1284	18.9%
R	1011	14.9%
D	802	11.8%
S	684	10.1%
E	608	9.0%
N	322	4.7%
B	299	4.4%
P	271	4.0%
I	228	3.4%
G	190	2.8%
Other values (16)	1084	16.0%

Most occurring categories

Value	Count	Frequency (%)
Uppercase Letter	6783	100.0%

Most frequent character per category

Uppercase Letter

Value	Count	Frequency (%)
U	1284	18.9%
R	1011	14.9%
D	802	11.8%
S	684	10.1%
E	608	9.0%
N	322	4.7%
B	299	4.4%
P	271	4.0%
I	228	3.4%
G	190	2.8%
Other values (16)	1084	16.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	6783	100.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
U	1284	18.9%
R	1011	14.9%
D	802	11.8%
S	684	10.1%
E	608	9.0%
N	322	4.7%
B	299	4.4%
P	271	4.0%
I	228	3.4%
G	190	2.8%
Other values (16)	1084	16.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	6783	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
U	1284	18.9%
R	1011	14.9%
D	802	11.8%
S	684	10.1%
E	608	9.0%
N	322	4.7%
B	299	4.4%
P	271	4.0%
I	228	3.4%
G	190	2.8%
Other values (16)	1084	16.0%

database_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct	576
Distinct (%)	34.1%
Missing	574
Missing (%)	25.4%
Memory size	17.8 KiB

PostgreSQL	124
PostgreSQL;SQLite	57
MongoDB	54
MySQL	49
Microsoft SQL Server	49
Other values (571)	1354

Length

Max length	133
Median length	85
Mean length	28.02489627
Min length	5

Characters and Unicode

Total characters	47278
Distinct characters	35
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	376 ?
Unique (%)	22.3%

Sample

1st row	Microsoft SQL Server;MongoDB;SQLite
2nd row	PostgreSQL;SQLite
3rd row	PostgreSQL
4th row	PostgreSQL
5th row	Redis

Common Values

Value	Count	Frequency (%)
PostgreSQL	124	5.5%
PostgreSQL;SQLite	57	2.5%
MongoDB	54	2.4%
MySQL	49	2.2%
Microsoft SQL Server	49	2.2%
Elasticsearch;PostgreSQL	34	1.5%
PostgreSQL;Redis	34	1.5%
SQLite	34	1.5%
MongoDB;PostgreSQL	25	1.1%
Elasticsearch	23	1.0%
Other values (566)	1204	53.3%
(Missing)	574	25.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
sql	342	14.2%
microsoft	175	7.2%
postgresql	124	5.1%
server	62	2.6%
postgresql;sqlite	57	2.4%
mongodb	54	2.2%
mysql	49	2.0%
elasticsearch;microsoft	37	1.5%
elasticsearch;postgresql	34	1.4%
postgresql;redis	34	1.4%
Other values (491)	1447	59.9%

Most occurring characters

Value	Count	Frequency (%)
e	3722	7.9%
s	3525	7.5%
;	3359	7.1%
r	3254	6.9%
o	3133	6.6%
a	2826	6.0%
S	2585	5.5%
i	2254	4.8%
L	2243	4.7%
Q	2243	4.7%
Other values (25)	18134	38.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	29238	61.8%
Uppercase Letter	13909	29.4%
Other Punctuation	3359	7.1%
Space Separator	728	1.5%
Decimal Number	44	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3722	12.7%
s	3525	12.1%
r	3254	11.1%
o	3133	10.7%
a	2826	9.7%
i	2254	7.7%
t	2241	7.7%
c	1662	5.7%
g	1529	5.2%
n	1085	3.7%
Other values (9)	4007	13.7%

Uppercase Letter

Value	Count	Frequency (%)
S	2585	18.6%
L	2243	16.1%
Q	2243	16.1%
M	1776	12.8%
D	1293	9.3%
B	1138	8.2%
P	878	6.3%
E	537	3.9%
R	489	3.5%
C	305	2.2%
Other values (3)	422	3.0%

Other Punctuation

Value	Count	Frequency (%)
;	3359	100.0%

Space Separator

Value	Count	Frequency (%)
	728	100.0%

Decimal Number

Value	Count	Frequency (%)
2	44	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	43147	91.3%
Common	4131	8.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3722	8.6%
s	3525	8.2%
r	3254	7.5%
o	3133	7.3%
a	2826	6.5%
S	2585	6.0%
i	2254	5.2%
L	2243	5.2%
Q	2243	5.2%
t	2241	5.2%
Other values (22)	15121	35.0%

Common

Value	Count	Frequency (%)
;	3359	81.3%
	728	17.6%
2	44	1.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	47278	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	3722	7.9%
s	3525	7.5%
;	3359	7.1%
r	3254	6.9%
o	3133	6.6%
a	2826	6.0%
S	2585	5.5%
i	2254	4.8%
L	2243	4.7%
Q	2243	4.7%
Other values (25)	18134	38.4%

database_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct	594
Distinct (%)	30.7%
Missing	327
Missing (%)	14.5%
Memory size	17.8 KiB

PostgreSQL	116
Microsoft SQL Server	105
MySQL	86
SQLite	43
MySQL;PostgreSQL	42
Other values (589)	1542

Length

Max length	133
Median length	92
Mean length	29.38883144
Min length	5

Characters and Unicode

Total characters	56838
Distinct characters	35
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	374 ?
Unique (%)	19.3%

Sample

1st row	IBM DB2;Microsoft SQL Server;MongoDB;SQLite
2nd row	Microsoft SQL Server;Oracle;PostgreSQL;SQLite
3rd row	MongoDB
4th row	PostgreSQL
5th row	MariaDB

Common Values

Value	Count	Frequency (%)
PostgreSQL	116	5.1%
Microsoft SQL Server	105	4.6%
MySQL	86	3.8%
SQLite	43	1.9%
MySQL;PostgreSQL	42	1.9%
PostgreSQL;SQLite	40	1.8%
MySQL;SQLite	36	1.6%
MySQL;PostgreSQL;SQLite	35	1.5%
MongoDB	34	1.5%
Oracle	28	1.2%
Other values (584)	1369	60.5%
(Missing)	327	14.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
sql	656	19.8%
microsoft	373	11.2%
server	117	3.5%
postgresql	116	3.5%
mysql	86	2.6%
elasticsearch;microsoft	58	1.7%
ibm	50	1.5%
mariadb;microsoft	49	1.5%
sqlite	43	1.3%
mysql;postgresql	42	1.3%
Other values (445)	1727	52.1%

Most occurring characters

Value	Count	Frequency (%)
e	4614	8.1%
r	4313	7.6%
S	4086	7.2%
;	4075	7.2%
o	3700	6.5%
Q	3430	6.0%
L	3430	6.0%
s	3274	5.8%
t	2831	5.0%
i	2686	4.7%
Other values (25)	20399	35.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	32735	57.6%
Uppercase Letter	18574	32.7%
Other Punctuation	4075	7.2%
Space Separator	1383	2.4%
Decimal Number	71	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	4614	14.1%
r	4313	13.2%
o	3700	11.3%
s	3274	10.0%
t	2831	8.6%
i	2686	8.2%
a	2444	7.5%
c	1859	5.7%
g	1601	4.9%
y	1157	3.5%
Other values (9)	4256	13.0%

Uppercase Letter

Value	Count	Frequency (%)
S	4086	22.0%
Q	3430	18.5%
L	3430	18.5%
M	2630	14.2%
D	1273	6.9%
B	1192	6.4%
P	1004	5.4%
E	406	2.2%
R	380	2.0%
O	353	1.9%
Other values (3)	390	2.1%

Other Punctuation

Value	Count	Frequency (%)
;	4075	100.0%

Space Separator

Value	Count	Frequency (%)
	1383	100.0%

Decimal Number

Value	Count	Frequency (%)
2	71	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	51309	90.3%
Common	5529	9.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	4614	9.0%
r	4313	8.4%
S	4086	8.0%
o	3700	7.2%
Q	3430	6.7%
L	3430	6.7%
s	3274	6.4%
t	2831	5.5%
i	2686	5.2%
M	2630	5.1%
Other values (22)	16315	31.8%

Common

Value	Count	Frequency (%)
;	4075	73.7%
	1383	25.0%
2	71	1.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	56838	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	4614	8.1%
r	4313	7.6%
S	4086	7.2%
;	4075	7.2%
o	3700	6.5%
Q	3430	6.0%
L	3430	6.0%
s	3274	5.8%
t	2831	5.0%
i	2686	4.7%
Other values (25)	20399	35.9%

dev_type
Categorical

HIGH CARDINALITY

Distinct	995
Distinct (%)	44.0%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

Data scientist or machine learning specialist	198
Data or business analyst;Data scientist or machine learning specialist	115
Data scientist or machine learning specialist;Developer, back-end	88
Academic researcher;Data scientist or machine learning specialist	59
Academic researcher;Data scientist or machine learning specialist;Scientist	53
Other values (990)	1748

Length

Max length	531
Median length	364
Mean length	118.5696594
Min length	45

Characters and Unicode

Total characters	268086
Distinct characters	36
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	806 ?
Unique (%)	35.6%

Sample

1st row	Data or business analyst;Data scientist or machine learning specialist
2nd row	Data scientist or machine learning specialist;Developer, back-end;Developer, QA or test;Engineer, data;Scientist
3rd row	Data scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, full-stack;Engineer, data
4th row	Data scientist or machine learning specialist;Scientist
5th row	Data scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, front-end;Developer, full-stack

Common Values

Value	Count	Frequency (%)
Data scientist or machine learning specialist	198	8.8%
Data or business analyst;Data scientist or machine learning specialist	115	5.1%
Data scientist or machine learning specialist;Developer, back-end	88	3.9%
Academic researcher;Data scientist or machine learning specialist	59	2.6%
Academic researcher;Data scientist or machine learning specialist;Scientist	53	2.3%
Data scientist or machine learning specialist;Developer, full-stack	48	2.1%
Data scientist or machine learning specialist;Engineer, data	46	2.0%
Data scientist or machine learning specialist;Developer, back-end;Engineer, data	40	1.8%
Data or business analyst;Data scientist or machine learning specialist;Engineer, data	37	1.6%
Data scientist or machine learning specialist;Scientist	36	1.6%
Other values (985)	1541	68.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
or	3875	15.9%
scientist	2261	9.3%
learning	2261	9.3%
machine	2261	9.3%
data	2028	8.3%
specialist;developer	1051	4.3%
back-end;developer	709	2.9%
business	680	2.8%
analyst;data	680	2.8%
academic	585	2.4%
Other values (179)	8022	32.9%

Most occurring characters

Value	Count	Frequency (%)
e	32112	12.0%
a	23745	8.9%
	22152	8.3%
i	22006	8.2%
s	18221	6.8%
t	18039	6.7%
n	17162	6.4%
r	15992	6.0%
c	12715	4.7%
l	11701	4.4%
Other values (26)	74241	27.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	220731	82.3%
Space Separator	22152	8.3%
Other Punctuation	12110	4.5%
Uppercase Letter	10820	4.0%
Dash Punctuation	2273	0.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	32112	14.5%
a	23745	10.8%
i	22006	10.0%
s	18221	8.3%
t	18039	8.2%
n	17162	7.8%
r	15992	7.2%
c	12715	5.8%
l	11701	5.3%
o	10172	4.6%
Other values (12)	38866	17.6%

Uppercase Letter

Value	Count	Frequency (%)
D	7223	66.8%
E	1214	11.2%
S	859	7.9%
A	731	6.8%
O	314	2.9%
P	227	2.1%
Q	146	1.3%
V	79	0.7%
M	27	0.2%

Other Punctuation

Value	Count	Frequency (%)
;	7795	64.4%
,	4236	35.0%
/	79	0.7%

Space Separator

Value	Count	Frequency (%)
	22152	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2273	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	231551	86.4%
Common	36535	13.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	32112	13.9%
a	23745	10.3%
i	22006	9.5%
s	18221	7.9%
t	18039	7.8%
n	17162	7.4%
r	15992	6.9%
c	12715	5.5%
l	11701	5.1%
o	10172	4.4%
Other values (21)	49686	21.5%

Common

Value	Count	Frequency (%)
	22152	60.6%
;	7795	21.3%
,	4236	11.6%
-	2273	6.2%
/	79	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	268086	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	32112	12.0%
a	23745	8.9%
	22152	8.3%
i	22006	8.2%
s	18221	6.8%
t	18039	6.7%
n	17162	6.4%
r	15992	6.0%
c	12715	4.7%
l	11701	4.4%
Other values (26)	74241	27.7%

ed_level
Categorical

Distinct	9
Distinct (%)	0.4%
Missing	15
Missing (%)	0.7%
Memory size	17.8 KiB

Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	961
Bachelor’s degree (B.A., B.S., B.Eng., etc.)	743
Other doctoral degree (Ph.D., Ed.D., etc.)	346
Some college/university study without earning a degree	125
Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)	27
Other values (4)	44

Length

Max length	82
Median length	54
Mean length	45.78895815
Min length	25

Characters and Unicode

Total characters	102842
Distinct characters	39
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Some college/university study without earning a degree
2nd row	Other doctoral degree (Ph.D., Ed.D., etc.)
3rd row	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)
4th row	Other doctoral degree (Ph.D., Ed.D., etc.)
5th row	Bachelor’s degree (B.A., B.S., B.Eng., etc.)

Common Values

Value	Count	Frequency (%)
Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	961	42.5%
Bachelor’s degree (B.A., B.S., B.Eng., etc.)	743	32.9%
Other doctoral degree (Ph.D., Ed.D., etc.)	346	15.3%
Some college/university study without earning a degree	125	5.5%
Secondary school (e.g. American high school, German Realschule or Gymnasium, etc.)	27	1.2%
Professional degree (JD, MD, etc.)	18	0.8%
Associate degree (A.A., A.S., etc.)	16	0.7%
I never completed any formal education	5	0.2%
Primary/elementary school	5	0.2%
(Missing)	15	0.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
degree	2209	15.1%
etc	2111	14.4%
master’s	961	6.6%
m.a	961	6.6%
m.s	961	6.6%
m.eng	961	6.6%
mba	961	6.6%
bachelor’s	743	5.1%
b.a	743	5.1%
b.s	743	5.1%
Other values (33)	3289	22.5%

Most occurring characters

Value	Count	Frequency (%)
.	13837	13.5%
	12397	12.1%
e	11649	11.3%
,	6887	6.7%
r	5006	4.9%
M	4823	4.7%
t	4295	4.2%
g	4217	4.1%
B	3933	3.8%
c	3491	3.4%
Other values (29)	32307	31.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	46684	45.4%
Other Punctuation	20854	20.3%
Uppercase Letter	16981	16.5%
Space Separator	12397	12.1%
Close Punctuation	2111	2.1%
Open Punctuation	2111	2.1%
Final Punctuation	1704	1.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	11649	25.0%
r	5006	10.7%
t	4295	9.2%
g	4217	9.0%
c	3491	7.5%
s	3096	6.6%
d	3063	6.6%
a	2494	5.3%
n	2225	4.8%
o	2049	4.4%
Other values (10)	5099	10.9%

Uppercase Letter

Value	Count	Frequency (%)
M	4823	28.4%
B	3933	23.2%
A	2756	16.2%
E	2050	12.1%
S	1872	11.0%
D	728	4.3%
P	369	2.2%
O	346	2.0%
G	54	0.3%
R	27	0.2%
Other values (2)	23	0.1%

Other Punctuation

Value	Count	Frequency (%)
.	13837	66.4%
,	6887	33.0%
/	130	0.6%

Space Separator

Value	Count	Frequency (%)
	12397	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	2111	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	2111	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1704	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	63665	61.9%
Common	39177	38.1%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	11649	18.3%
r	5006	7.9%
M	4823	7.6%
t	4295	6.7%
g	4217	6.6%
B	3933	6.2%
c	3491	5.5%
s	3096	4.9%
d	3063	4.8%
A	2756	4.3%
Other values (22)	17336	27.2%

Common

Value	Count	Frequency (%)
.	13837	35.3%
	12397	31.6%
,	6887	17.6%
)	2111	5.4%
(	2111	5.4%
’	1704	4.3%
/	130	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	101138	98.3%
Punctuation	1704	1.7%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
.	13837	13.7%
	12397	12.3%
e	11649	11.5%
,	6887	6.8%
r	5006	4.9%
M	4823	4.8%
t	4295	4.2%
g	4217	4.2%
B	3933	3.9%
c	3491	3.5%
Other values (28)	30603	30.3%

Punctuation

Value	Count	Frequency (%)
’	1704	100.0%

employment
Categorical

HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

Employed full-time	1965
Independent contractor, freelancer, or self-employed	200
Employed part-time	96

Length

Max length	52
Median length	18
Mean length	21.0075188
Min length	18

Characters and Unicode

Total characters	47498
Distinct characters	21
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Employed full-time
2nd row	Employed full-time
3rd row	Employed full-time
4th row	Employed full-time
5th row	Employed full-time

Common Values

Value	Count	Frequency (%)
Employed full-time	1965	86.9%
Independent contractor, freelancer, or self-employed	200	8.8%
Employed part-time	96	4.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
employed	2061	40.2%
full-time	1965	38.4%
independent	200	3.9%
contractor	200	3.9%
freelancer	200	3.9%
or	200	3.9%
self-employed	200	3.9%
part-time	96	1.9%

Most occurring characters

Value	Count	Frequency (%)
l	6591	13.9%
e	5922	12.5%
m	4322	9.1%
o	2861	6.0%
	2861	6.0%
t	2757	5.8%
d	2661	5.6%
p	2557	5.4%
f	2365	5.0%
y	2261	4.8%
Other values (11)	12340	26.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	39715	83.6%
Space Separator	2861	6.0%
Dash Punctuation	2261	4.8%
Uppercase Letter	2261	4.8%
Other Punctuation	400	0.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
l	6591	16.6%
e	5922	14.9%
m	4322	10.9%
o	2861	7.2%
t	2757	6.9%
d	2661	6.7%
p	2557	6.4%
f	2365	6.0%
y	2261	5.7%
i	2061	5.2%
Other values (6)	5357	13.5%

Uppercase Letter

Value	Count	Frequency (%)
E	2061	91.2%
I	200	8.8%

Space Separator

Value	Count	Frequency (%)
	2861	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2261	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	400	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	41976	88.4%
Common	5522	11.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
l	6591	15.7%
e	5922	14.1%
m	4322	10.3%
o	2861	6.8%
t	2757	6.6%
d	2661	6.3%
p	2557	6.1%
f	2365	5.6%
y	2261	5.4%
E	2061	4.9%
Other values (8)	7618	18.1%

Common

Value	Count	Frequency (%)
	2861	51.8%
-	2261	40.9%
,	400	7.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	47498	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
l	6591	13.9%
e	5922	12.5%
m	4322	9.1%
o	2861	6.0%
	2861	6.0%
t	2757	5.8%
d	2661	5.6%
p	2557	5.4%
f	2365	5.0%
y	2261	4.8%
Other values (11)	12340	26.0%

ethnicity
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	51
Distinct (%)	2.4%
Missing	169
Missing (%)	7.5%
Memory size	17.8 KiB

White or of European descent	1443
South Asian	192
Hispanic or Latino/a/x	95
Middle Eastern	60
East Asian	55
Other values (46)	247

Length

Max length	235
Median length	28
Mean length	26.86137667
Min length	8

Characters and Unicode

Total characters	56194
Distinct characters	37
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	26 ?
Unique (%)	1.2%

Sample

1st row	White or of European descent
2nd row	White or of European descent
3rd row	White or of European descent
4th row	White or of European descent
5th row	White or of European descent

Common Values

Value	Count	Frequency (%)
White or of European descent	1443	63.8%
South Asian	192	8.5%
Hispanic or Latino/a/x	95	4.2%
Middle Eastern	60	2.7%
East Asian	55	2.4%
Southeast Asian	50	2.2%
Hispanic or Latino/a/x;White or of European descent	44	1.9%
Black or of African descent	40	1.8%
Middle Eastern;White or of European descent	18	0.8%
Multiracial	11	0.5%
Other values (41)	84	3.7%
(Missing)	169	7.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
or	1803	19.0%
of	1624	17.1%
european	1561	16.5%
descent	1554	16.4%
white	1459	15.4%
asian	310	3.3%
south	194	2.0%
hispanic	156	1.6%
latino/a/x	98	1.0%
middle	81	0.9%
Other values (46)	634	6.7%

Most occurring characters

Value	Count	Frequency (%)
	7382	13.1%
e	6686	11.9%
o	5443	9.7%
n	4104	7.3%
t	3906	7.0%
r	3630	6.5%
a	2981	5.3%
i	2782	5.0%
s	2437	4.3%
c	2043	3.6%
Other values (27)	14800	26.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	43625	77.6%
Space Separator	7382	13.1%
Uppercase Letter	4596	8.2%
Other Punctuation	561	1.0%
Open Punctuation	15	< 0.1%
Close Punctuation	15	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	6686	15.3%
o	5443	12.5%
n	4104	9.4%
t	3906	9.0%
r	3630	8.3%
a	2981	6.8%
i	2782	6.4%
s	2437	5.6%
c	2043	4.7%
u	1923	4.4%
Other values (10)	7690	17.6%

Uppercase Letter

Value	Count	Frequency (%)
E	1723	37.5%
W	1561	34.0%
A	427	9.3%
S	261	5.7%
H	164	3.6%
L	164	3.6%
M	130	2.8%
B	91	2.0%
I	45	1.0%
N	15	0.3%

Other Punctuation

Value	Count	Frequency (%)
/	328	58.5%
;	203	36.2%
,	30	5.3%

Space Separator

Value	Count	Frequency (%)
	7382	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	15	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	15	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	48221	85.8%
Common	7973	14.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	6686	13.9%
o	5443	11.3%
n	4104	8.5%
t	3906	8.1%
r	3630	7.5%
a	2981	6.2%
i	2782	5.8%
s	2437	5.1%
c	2043	4.2%
u	1923	4.0%
Other values (21)	12286	25.5%

Common

Value	Count	Frequency (%)
	7382	92.6%
/	328	4.1%
;	203	2.5%
,	30	0.4%
(	15	0.2%
)	15	0.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	56194	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	7382	13.1%
e	6686	11.9%
o	5443	9.7%
n	4104	7.3%
t	3906	7.0%
r	3630	6.5%
a	2981	5.3%
i	2782	5.0%
s	2437	4.3%
c	2043	3.6%
Other values (27)	14800	26.3%

gender
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	6
Distinct (%)	0.3%
Missing	33
Missing (%)	1.5%
Memory size	17.8 KiB

Man	1997
Woman	199
Non-binary, genderqueer, or gender non-conforming	16
Man;Non-binary, genderqueer, or gender non-conforming	10
Woman;Non-binary, genderqueer, or gender non-conforming	5

Length

Max length	59
Median length	3
Mean length	3.875224417
Min length	3

Characters and Unicode

Total characters	8634
Distinct characters	22
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	Man
2nd row	Man
3rd row	Man
4th row	Man
5th row	Man

Common Values

Value	Count	Frequency (%)
Man	1997	88.3%
Woman	199	8.8%
Non-binary, genderqueer, or gender non-conforming	16	0.7%
Man;Non-binary, genderqueer, or gender non-conforming	10	0.4%
Woman;Non-binary, genderqueer, or gender non-conforming	5	0.2%
Woman;Man;Non-binary, genderqueer, or gender non-conforming	1	< 0.1%
(Missing)	33	1.5%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
man	1997	84.8%
woman	199	8.4%
genderqueer	32	1.4%
or	32	1.4%
gender	32	1.4%
non-conforming	32	1.4%
non-binary	16	0.7%
man;non-binary	10	0.4%
woman;non-binary	5	0.2%
woman;man;non-binary	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
n	2469	28.6%
a	2245	26.0%
M	2008	23.3%
o	365	4.2%
m	237	2.7%
W	205	2.4%
e	192	2.2%
r	192	2.2%
	128	1.5%
g	96	1.1%
Other values (12)	497	5.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6116	70.8%
Uppercase Letter	2245	26.0%
Space Separator	128	1.5%
Other Punctuation	81	0.9%
Dash Punctuation	64	0.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
n	2469	40.4%
a	2245	36.7%
o	365	6.0%
m	237	3.9%
e	192	3.1%
r	192	3.1%
g	96	1.6%
d	64	1.0%
i	64	1.0%
u	32	0.5%
Other values (5)	160	2.6%

Uppercase Letter

Value	Count	Frequency (%)
M	2008	89.4%
W	205	9.1%
N	32	1.4%

Other Punctuation

Value	Count	Frequency (%)
,	64	79.0%
;	17	21.0%

Space Separator

Value	Count	Frequency (%)
	128	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	64	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8361	96.8%
Common	273	3.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
n	2469	29.5%
a	2245	26.9%
M	2008	24.0%
o	365	4.4%
m	237	2.8%
W	205	2.5%
e	192	2.3%
r	192	2.3%
g	96	1.1%
d	64	0.8%
Other values (8)	288	3.4%

Common

Value	Count	Frequency (%)
	128	46.9%
,	64	23.4%
-	64	23.4%
;	17	6.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	8634	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
n	2469	28.6%
a	2245	26.0%
M	2008	23.3%
o	365	4.2%
m	237	2.7%
W	205	2.4%
e	192	2.2%
r	192	2.2%
	128	1.5%
g	96	1.1%
Other values (12)	497	5.8%

job_factors
Categorical

HIGH CARDINALITY
MISSING

Distinct	180
Distinct (%)	8.1%
Missing	28
Missing (%)	1.2%
Memory size	17.8 KiB

How widely used or impactful my work output would be;Office environment or company culture;Opportunities for professional development	86
Languages, frameworks, and other technologies I’d be working with;Office environment or company culture;Opportunities for professional development	78
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Remote work options	74
Flex time or a flexible schedule;Office environment or company culture;Opportunities for professional development	71
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Office environment or company culture	68
Other values (175)	1856

Length

Max length	189
Median length	155
Mean length	124.1988356
Min length	19

Characters and Unicode

Total characters	277336
Distinct characters	36
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	24 ?
Unique (%)	1.1%

Sample

1st row	Flex time or a flexible schedule;Office environment or company culture;Opportunities for professional development
2nd row	Remote work options;How widely used or impactful my work output would be;Opportunities for professional development
3rd row	Flex time or a flexible schedule;How widely used or impactful my work output would be;Opportunities for professional development
4th row	Flex time or a flexible schedule;Family friendliness
5th row	Diversity of the company or organization;Flex time or a flexible schedule;Office environment or company culture

Common Values

Value	Count	Frequency (%)
How widely used or impactful my work output would be;Office environment or company culture;Opportunities for professional development	86	3.8%
Languages, frameworks, and other technologies I’d be working with;Office environment or company culture;Opportunities for professional development	78	3.4%
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Remote work options	74	3.3%
Flex time or a flexible schedule;Office environment or company culture;Opportunities for professional development	71	3.1%
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Office environment or company culture	68	3.0%
Flex time or a flexible schedule;Remote work options;Office environment or company culture	65	2.9%
Flex time or a flexible schedule;Remote work options;Opportunities for professional development	63	2.8%
Languages, frameworks, and other technologies I’d be working with;How widely used or impactful my work output would be;Office environment or company culture	61	2.7%
Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Opportunities for professional development	51	2.3%
Languages, frameworks, and other technologies I’d be working with;How widely used or impactful my work output would be;Opportunities for professional development	46	2.0%
Other values (170)	1570	69.4%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
or	3591	9.8%
be	1908	5.2%
working	1703	4.7%
i’d	1703	4.7%
work	1383	3.8%
company	1363	3.7%
for	1007	2.8%
professional	1007	2.8%
environment	961	2.6%
development	926	2.5%
Other values (89)	20972	57.4%

Most occurring characters

Value	Count	Frequency (%)
	34291	12.4%
e	27005	9.7%
o	23539	8.5%
n	16432	5.9%
r	16393	5.9%
i	16145	5.8%
t	15883	5.7%
a	10727	3.9%
l	10400	3.7%
s	9579	3.5%
Other values (26)	96942	35.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	226842	81.8%
Space Separator	34291	12.4%
Uppercase Letter	8332	3.0%
Other Punctuation	6168	2.2%
Final Punctuation	1703	0.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	27005	11.9%
o	23539	10.4%
n	16432	7.2%
r	16393	7.2%
i	16145	7.1%
t	15883	7.0%
a	10727	4.7%
l	10400	4.6%
s	9579	4.2%
u	9197	4.1%
Other values (14)	71542	31.5%

Uppercase Letter

Value	Count	Frequency (%)
I	2151	25.8%
O	1968	23.6%
F	1408	16.9%
L	886	10.6%
H	710	8.5%
R	673	8.1%
S	369	4.4%
D	167	2.0%

Other Punctuation

Value	Count	Frequency (%)
;	4396	71.3%
,	1772	28.7%

Space Separator

Value	Count	Frequency (%)
	34291	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1703	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	235174	84.8%
Common	42162	15.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	27005	11.5%
o	23539	10.0%
n	16432	7.0%
r	16393	7.0%
i	16145	6.9%
t	15883	6.8%
a	10727	4.6%
l	10400	4.4%
s	9579	4.1%
u	9197	3.9%
Other values (22)	79874	34.0%

Common

Value	Count	Frequency (%)
	34291	81.3%
;	4396	10.4%
,	1772	4.2%
’	1703	4.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	275633	99.4%
Punctuation	1703	0.6%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	34291	12.4%
e	27005	9.8%
o	23539	8.5%
n	16432	6.0%
r	16393	5.9%
i	16145	5.9%
t	15883	5.8%
a	10727	3.9%
l	10400	3.8%
s	9579	3.5%
Other values (25)	95239	34.6%

Punctuation

Value	Count	Frequency (%)
’	1703	100.0%

job_sat
Categorical

Distinct	5
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	2.5 KiB

Very satisfied	879
Slightly satisfied	680
Slightly dissatisfied	342
Neither	201
Very dissatisfied	159

Length

Max length	21
Median length	18
Mean length	15.85050862
Min length	7

Characters and Unicode

Total characters	35838
Distinct characters	16
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Slightly satisfied
2nd row	Very satisfied
3rd row	Very satisfied
4th row	Very satisfied
5th row	Very satisfied

Common Values

Value	Count	Frequency (%)
Very satisfied	879	38.9%
Slightly satisfied	680	30.1%
Slightly dissatisfied	342	15.1%
Neither	201	8.9%
Very dissatisfied	159	7.0%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
satisfied	1559	36.1%
very	1038	24.0%
slightly	1022	23.7%
dissatisfied	501	11.6%
neither	201	4.7%

Most occurring characters

Value	Count	Frequency (%)
i	5844	16.3%
s	4621	12.9%
e	3500	9.8%
t	3283	9.2%
d	2561	7.1%
y	2060	5.7%
	2060	5.7%
a	2060	5.7%
f	2060	5.7%
l	2044	5.7%
Other values (6)	5745	16.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	31517	87.9%
Uppercase Letter	2261	6.3%
Space Separator	2060	5.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
i	5844	18.5%
s	4621	14.7%
e	3500	11.1%
t	3283	10.4%
d	2561	8.1%
y	2060	6.5%
a	2060	6.5%
f	2060	6.5%
l	2044	6.5%
r	1239	3.9%
Other values (2)	2245	7.1%

Uppercase Letter

Value	Count	Frequency (%)
V	1038	45.9%
S	1022	45.2%
N	201	8.9%

Space Separator

Value	Count	Frequency (%)
	2060	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	33778	94.3%
Common	2060	5.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
i	5844	17.3%
s	4621	13.7%
e	3500	10.4%
t	3283	9.7%
d	2561	7.6%
y	2060	6.1%
a	2060	6.1%
f	2060	6.1%
l	2044	6.1%
r	1239	3.7%
Other values (5)	4506	13.3%

Common

Value	Count	Frequency (%)
	2060	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	35838	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
i	5844	16.3%
s	4621	12.9%
e	3500	9.8%
t	3283	9.2%
d	2561	7.1%
y	2060	5.7%
	2060	5.7%
a	2060	5.7%
f	2060	5.7%
l	2044	5.7%
Other values (6)	5745	16.0%

job_seek
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

I’m not actively looking, but I am open to new opportunities	1330
I am not interested in new job opportunities	590
I am actively looking for a job	341

Length

Max length	60
Median length	60
Mean length	51.45112782
Min length	31

Characters and Unicode

Total characters	116331
Distinct characters	26
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	2 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	I’m not actively looking, but I am open to new opportunities
2nd row	I’m not actively looking, but I am open to new opportunities
3rd row	I am not interested in new job opportunities
4th row	I am not interested in new job opportunities
5th row	I am not interested in new job opportunities

Common Values

Value	Count	Frequency (%)
I’m not actively looking, but I am open to new opportunities	1330	58.8%
I am not interested in new job opportunities	590	26.1%
I am actively looking for a job	341	15.1%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
i	2261	10.4%
am	2261	10.4%
not	1920	8.8%
new	1920	8.8%
opportunities	1920	8.8%
actively	1671	7.7%
looking	1671	7.7%
i’m	1330	6.1%
but	1330	6.1%
open	1330	6.1%
Other values (6)	4123	19.0%

Most occurring characters

Value	Count	Frequency (%)
	19476	16.7%
o	13034	11.2%
t	11271	9.7%
n	9941	8.5%
e	8611	7.4%
i	8362	7.2%
p	5170	4.4%
a	4273	3.7%
I	3591	3.1%
m	3591	3.1%
Other values (16)	29011	24.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	90604	77.9%
Space Separator	19476	16.7%
Uppercase Letter	3591	3.1%
Other Punctuation	1330	1.1%
Final Punctuation	1330	1.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	13034	14.4%
t	11271	12.4%
n	9941	11.0%
e	8611	9.5%
i	8362	9.2%
p	5170	5.7%
a	4273	4.7%
m	3591	4.0%
l	3342	3.7%
u	3250	3.6%
Other values (12)	19759	21.8%

Space Separator

Value	Count	Frequency (%)
	19476	100.0%

Uppercase Letter

Value	Count	Frequency (%)
I	3591	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	1330	100.0%

Final Punctuation

Value	Count	Frequency (%)
’	1330	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	94195	81.0%
Common	22136	19.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	13034	13.8%
t	11271	12.0%
n	9941	10.6%
e	8611	9.1%
i	8362	8.9%
p	5170	5.5%
a	4273	4.5%
I	3591	3.8%
m	3591	3.8%
l	3342	3.5%
Other values (13)	23009	24.4%

Common

Value	Count	Frequency (%)
	19476	88.0%
,	1330	6.0%
’	1330	6.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	115001	98.9%
Punctuation	1330	1.1%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	19476	16.9%
o	13034	11.3%
t	11271	9.8%
n	9941	8.6%
e	8611	7.5%
i	8362	7.3%
p	5170	4.5%
a	4273	3.7%
I	3591	3.1%
m	3591	3.1%
Other values (15)	27681	24.1%

Punctuation

Value	Count	Frequency (%)
’	1330	100.0%

language_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct	1335
Distinct (%)	61.8%
Missing	101
Missing (%)	4.5%
Memory size	17.8 KiB

Python	92
Python;R;SQL	41
Python;R	33
Python;SQL	31
Bash/Shell/PowerShell;Python;SQL	21
Other values (1330)	1942

Length

Max length	164
Median length	82
Mean length	30.74675926
Min length	1

Characters and Unicode

Total characters	66413
Distinct characters	43
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1088 ?
Unique (%)	50.4%

Sample

1st row	C#;Go;HTML/CSS;JavaScript;Python;SQL
2nd row	Bash/Shell/PowerShell;Java;Python;SQL
3rd row	Python;Rust;Scala;SQL
4th row	Python
5th row	Rust

Common Values

Value	Count	Frequency (%)
Python	92	4.1%
Python;R;SQL	41	1.8%
Python;R	33	1.5%
Python;SQL	31	1.4%
Bash/Shell/PowerShell;Python;SQL	21	0.9%
C++;Python	21	0.9%
Rust	21	0.9%
Julia	19	0.8%
Bash/Shell/PowerShell;Python	17	0.8%
Python;Rust	15	0.7%
Other values (1325)	1849	81.8%
(Missing)	101	4.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
python	92	4.3%
python;r;sql	41	1.9%
python;r	33	1.5%
python;sql	31	1.4%
bash/shell/powershell;python;sql	21	1.0%
c++;python	21	1.0%
rust	21	1.0%
julia	19	0.9%
bash/shell/powershell;python	17	0.8%
python;rust	15	0.7%
Other values (1323)	1849	85.6%

Most occurring characters

Value	Count	Frequency (%)
;	7531	11.3%
S	4970	7.5%
a	4067	6.1%
l	3980	6.0%
t	3779	5.7%
h	3660	5.5%
o	3072	4.6%
e	2776	4.2%
P	2629	4.0%
y	2231	3.4%
Other values (33)	27718	41.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	37219	56.0%
Uppercase Letter	18388	27.7%
Other Punctuation	9770	14.7%
Math Symbol	988	1.5%
Dash Punctuation	48	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	4067	10.9%
l	3980	10.7%
t	3779	10.2%
h	3660	9.8%
o	3072	8.3%
e	2776	7.5%
y	2231	6.0%
r	1940	5.2%
n	1887	5.1%
i	1863	5.0%
Other values (11)	7964	21.4%

Uppercase Letter

Value	Count	Frequency (%)
S	4970	27.0%
P	2629	14.3%
C	1716	9.3%
L	1488	8.1%
J	1402	7.6%
R	1104	6.0%
Q	934	5.1%
T	934	5.1%
H	842	4.6%
B	717	3.9%
Other values (7)	1652	9.0%

Other Punctuation

Value	Count	Frequency (%)
;	7531	77.1%
/	1882	19.3%
#	357	3.7%

Math Symbol

Value	Count	Frequency (%)
+	988	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	48	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	55607	83.7%
Common	10806	16.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	4970	8.9%
a	4067	7.3%
l	3980	7.2%
t	3779	6.8%
h	3660	6.6%
o	3072	5.5%
e	2776	5.0%
P	2629	4.7%
y	2231	4.0%
r	1940	3.5%
Other values (28)	22503	40.5%

Common

Value	Count	Frequency (%)
;	7531	69.7%
/	1882	17.4%
+	988	9.1%
#	357	3.3%
-	48	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	66413	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
;	7531	11.3%
S	4970	7.5%
a	4067	6.1%
l	3980	6.0%
t	3779	5.7%
h	3660	5.5%
o	3072	4.6%
e	2776	4.2%
P	2629	4.0%
y	2231	3.4%
Other values (33)	27718	41.7%

language_worked_with
Categorical

HIGH CARDINALITY

Distinct	1271
Distinct (%)	56.4%
Missing	7
Missing (%)	0.3%
Memory size	17.8 KiB

Python	81
Python;R;SQL	63
Python;SQL	46
Python;R	35
Bash/Shell/PowerShell;Python;SQL	31
Other values (1266)	1998

Length

Max length	143
Median length	89
Mean length	38.108252
Min length	1

Characters and Unicode

Total characters	85896
Distinct characters	43
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1025 ?
Unique (%)	45.5%

Sample

1st row	C#;Go;HTML/CSS;Java;JavaScript;Python;R;SQL
2nd row	Bash/Shell/PowerShell;C#;Java;JavaScript;Python;Ruby;SQL
3rd row	HTML/CSS;Python
4th row	Python;SQL
5th row	HTML/CSS;JavaScript;Python;TypeScript

Common Values

Value	Count	Frequency (%)
Python	81	3.6%
Python;R;SQL	63	2.8%
Python;SQL	46	2.0%
Python;R	35	1.5%
Bash/Shell/PowerShell;Python;SQL	31	1.4%
Bash/Shell/PowerShell;Python	27	1.2%
Bash/Shell/PowerShell;Python;R;SQL	26	1.1%
Bash/Shell/PowerShell;HTML/CSS;JavaScript;Python;SQL	22	1.0%
R	18	0.8%
HTML/CSS;JavaScript;Python	18	0.8%
Other values (1261)	1887	83.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
python	81	3.6%
python;r;sql	63	2.8%
python;sql	46	2.0%
python;r	35	1.6%
bash/shell/powershell;python;sql	31	1.4%
bash/shell/powershell;python	27	1.2%
bash/shell/powershell;python;r;sql	26	1.2%
bash/shell/powershell;html/css;javascript;python;sql	22	1.0%
r	18	0.8%
html/css;javascript;python	18	0.8%
Other values (1261)	1887	83.7%

Most occurring characters

Value	Count	Frequency (%)
;	9645	11.2%
S	7401	8.6%
a	5406	6.3%
h	5037	5.9%
l	4907	5.7%
e	3848	4.5%
t	3812	4.4%
P	3750	4.4%
o	3298	3.8%
/	3164	3.7%
Other values (33)	35628	41.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	44262	51.5%
Uppercase Letter	26990	31.4%
Other Punctuation	13271	15.5%
Math Symbol	1302	1.5%
Dash Punctuation	71	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	5406	12.2%
h	5037	11.4%
l	4907	11.1%
e	3848	8.7%
t	3812	8.6%
o	3298	7.5%
r	2618	5.9%
y	2492	5.6%
n	2039	4.6%
v	1939	4.4%
Other values (11)	8866	20.0%

Uppercase Letter

Value	Count	Frequency (%)
S	7401	27.4%
P	3750	13.9%
C	2775	10.3%
L	2505	9.3%
J	1963	7.3%
H	1509	5.6%
Q	1415	5.2%
T	1413	5.2%
B	1239	4.6%
M	1090	4.0%
Other values (7)	1930	7.2%

Other Punctuation

Value	Count	Frequency (%)
;	9645	72.7%
/	3164	23.8%
#	462	3.5%

Math Symbol

Value	Count	Frequency (%)
+	1302	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	71	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	71252	83.0%
Common	14644	17.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
S	7401	10.4%
a	5406	7.6%
h	5037	7.1%
l	4907	6.9%
e	3848	5.4%
t	3812	5.4%
P	3750	5.3%
o	3298	4.6%
C	2775	3.9%
r	2618	3.7%
Other values (28)	28400	39.9%

Common

Value	Count	Frequency (%)
;	9645	65.9%
/	3164	21.6%
+	1302	8.9%
#	462	3.2%
-	71	0.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	85896	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
;	9645	11.2%
S	7401	8.6%
a	5406	6.3%
h	5037	5.9%
l	4907	5.7%
e	3848	4.5%
t	3812	4.4%
P	3750	4.4%
o	3298	3.8%
/	3164	3.7%
Other values (33)	35628	41.5%

misc_tech_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct	783
Distinct (%)	40.1%
Missing	307
Missing (%)	13.6%
Memory size	17.8 KiB

Pandas	70
Keras;Pandas;TensorFlow;Torch/PyTorch	66
Keras;Pandas;TensorFlow	50
Pandas;TensorFlow	48
Apache Spark;Hadoop;Keras;Pandas;TensorFlow;Torch/PyTorch	46
Other values (778)	1674

Length

Max length	169
Median length	95
Mean length	35.44370522
Min length	4

Characters and Unicode

Total characters	69257
Distinct characters	42
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	559 ?
Unique (%)	28.6%

Sample

1st row	Keras;Node.js;Pandas;TensorFlow
2nd row	Pandas
3rd row	Keras;Pandas;TensorFlow
4th row	Keras;Pandas;TensorFlow;Torch/PyTorch
5th row	.NET Core

Common Values

Value	Count	Frequency (%)
Pandas	70	3.1%
Keras;Pandas;TensorFlow;Torch/PyTorch	66	2.9%
Keras;Pandas;TensorFlow	50	2.2%
Pandas;TensorFlow	48	2.1%
Apache Spark;Hadoop;Keras;Pandas;TensorFlow;Torch/PyTorch	46	2.0%
Pandas;Torch/PyTorch	44	1.9%
Torch/PyTorch	42	1.9%
Pandas;TensorFlow;Torch/PyTorch	40	1.8%
TensorFlow	35	1.5%
TensorFlow;Torch/PyTorch	33	1.5%
Other values (773)	1480	65.5%
(Missing)	307	13.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
apache	457	13.2%
net;.net	153	4.4%
net	150	4.3%
3d	122	3.5%
engine	117	3.4%
3d;unreal	101	2.9%
pandas	70	2.0%
keras;pandas;tensorflow;torch/pytorch	66	1.9%
ansible;apache	54	1.6%
core;apache	54	1.6%
Other values (636)	2106	61.0%

Most occurring characters

Value	Count	Frequency (%)
o	6282	9.1%
a	5676	8.2%
r	5573	8.0%
;	5552	8.0%
e	4832	7.0%
s	3802	5.5%
T	3764	5.4%
n	3258	4.7%
c	2636	3.8%
h	2452	3.5%
Other values (32)	25430	36.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	46987	67.8%
Uppercase Letter	13047	18.8%
Other Punctuation	7487	10.8%
Space Separator	1496	2.2%
Decimal Number	240	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	6282	13.4%
a	5676	12.1%
r	5573	11.9%
e	4832	10.3%
s	3802	8.1%
n	3258	6.9%
c	2636	5.6%
h	2452	5.2%
d	2134	4.5%
p	1746	3.7%
Other values (13)	8596	18.3%

Uppercase Letter

Value	Count	Frequency (%)
T	3764	28.8%
P	2090	16.0%
F	1403	10.8%
N	1265	9.7%
K	768	5.9%
A	749	5.7%
E	644	4.9%
S	585	4.5%
H	450	3.4%
C	385	3.0%
Other values (4)	944	7.2%

Other Punctuation

Value	Count	Frequency (%)
;	5552	74.2%
.	1028	13.7%
/	907	12.1%

Space Separator

Value	Count	Frequency (%)
	1496	100.0%

Decimal Number

Value	Count	Frequency (%)
3	240	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	60034	86.7%
Common	9223	13.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	6282	10.5%
a	5676	9.5%
r	5573	9.3%
e	4832	8.0%
s	3802	6.3%
T	3764	6.3%
n	3258	5.4%
c	2636	4.4%
h	2452	4.1%
d	2134	3.6%
Other values (27)	19625	32.7%

Common

Value	Count	Frequency (%)
;	5552	60.2%
	1496	16.2%
.	1028	11.1%
/	907	9.8%
3	240	2.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	69257	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	6282	9.1%
a	5676	8.2%
r	5573	8.0%
;	5552	8.0%
e	4832	7.0%
s	3802	5.5%
T	3764	5.4%
n	3258	4.7%
c	2636	3.8%
h	2452	3.5%
Other values (32)	25430	36.7%

misc_tech_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct	578
Distinct (%)	29.4%
Missing	297
Missing (%)	13.1%
Memory size	17.8 KiB

Pandas	223
Keras;Pandas;TensorFlow	97
Keras;Pandas;TensorFlow;Torch/PyTorch	72
Node.js	55
Pandas;TensorFlow	50
Other values (573)	1467

Length

Max length	140
Median length	82
Mean length	27.00763747
Min length	4

Characters and Unicode

Total characters	53043
Distinct characters	42
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	402 ?
Unique (%)	20.5%

Sample

1st row	Node.js;Pandas
2nd row	.NET;.NET Core
3rd row	Keras;Pandas;TensorFlow
4th row	Keras;Pandas;TensorFlow
5th row	Node.js;Pandas

Common Values

Value	Count	Frequency (%)
Pandas	223	9.9%
Keras;Pandas;TensorFlow	97	4.3%
Keras;Pandas;TensorFlow;Torch/PyTorch	72	3.2%
Node.js	55	2.4%
Pandas;TensorFlow	50	2.2%
Node.js;Pandas	43	1.9%
.NET	37	1.6%
.NET;.NET Core	35	1.5%
Keras;TensorFlow	33	1.5%
TensorFlow	32	1.4%
Other values (568)	1287	56.9%
(Missing)	297	13.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
apache	296	9.9%
net;.net	246	8.3%
pandas	223	7.5%
keras;pandas;tensorflow	97	3.3%
3d	91	3.1%
net	91	3.1%
keras;pandas;tensorflow;torch/pytorch	72	2.4%
node.js	55	1.8%
pandas;tensorflow	50	1.7%
core	46	1.5%
Other values (497)	1713	57.5%

Most occurring characters

Value	Count	Frequency (%)
a	4929	9.3%
o	4645	8.8%
;	4239	8.0%
r	3782	7.1%
e	3677	6.9%
s	3572	6.7%
T	2735	5.2%
n	2666	5.0%
d	2229	4.2%
P	1793	3.4%
Other values (32)	18776	35.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	35387	66.7%
Uppercase Letter	10506	19.8%
Other Punctuation	6000	11.3%
Space Separator	1016	1.9%
Decimal Number	134	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	4929	13.9%
o	4645	13.1%
r	3782	10.7%
e	3677	10.4%
s	3572	10.1%
n	2666	7.5%
d	2229	6.3%
c	1523	4.3%
h	1414	4.0%
l	1198	3.4%
Other values (13)	5752	16.3%

Uppercase Letter

Value	Count	Frequency (%)
T	2735	26.0%
P	1793	17.1%
N	1412	13.4%
F	1007	9.6%
E	731	7.0%
K	658	6.3%
A	534	5.1%
C	404	3.8%
S	390	3.7%
H	322	3.1%
Other values (4)	520	4.9%

Other Punctuation

Value	Count	Frequency (%)
;	4239	70.7%
.	1267	21.1%
/	494	8.2%

Space Separator

Value	Count	Frequency (%)
	1016	100.0%

Decimal Number

Value	Count	Frequency (%)
3	134	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	45893	86.5%
Common	7150	13.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	4929	10.7%
o	4645	10.1%
r	3782	8.2%
e	3677	8.0%
s	3572	7.8%
T	2735	6.0%
n	2666	5.8%
d	2229	4.9%
P	1793	3.9%
c	1523	3.3%
Other values (27)	14342	31.3%

Common

Value	Count	Frequency (%)
;	4239	59.3%
.	1267	17.7%
	1016	14.2%
/	494	6.9%
3	134	1.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	53043	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	4929	9.3%
o	4645	8.8%
;	4239	8.0%
r	3782	7.1%
e	3677	6.9%
s	3572	6.7%
T	2735	5.2%
n	2666	5.0%
d	2229	4.2%
P	1793	3.4%
Other values (32)	18776	35.4%

new_collab_tools_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct	402
Distinct (%)	19.9%
Missing	238
Missing (%)	10.5%
Memory size	17.8 KiB

Github	153
Github;Slack;Google Suite (Docs, Meet, etc)	104
Github;Gitlab	87
Github;Slack	77
Github;Google Suite (Docs, Meet, etc)	57
Other values (397)	1545

Length

Max length	149
Median length	114
Mean length	37.13049926
Min length	4

Characters and Unicode

Total characters	75115
Distinct characters	37
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	200 ?
Unique (%)	9.9%

Sample

1st row	Confluence;Jira;Github;Slack;Trello
2nd row	Github;Gitlab
3rd row	Github;Slack;Google Suite (Docs, Meet, etc)
4th row	Jira
5th row	Gitlab

Common Values

Value	Count	Frequency (%)
Github	153	6.8%
Github;Slack;Google Suite (Docs, Meet, etc)	104	4.6%
Github;Gitlab	87	3.8%
Github;Slack	77	3.4%
Github;Google Suite (Docs, Meet, etc)	57	2.5%
Github;Gitlab;Slack	57	2.5%
Github;Gitlab;Slack;Google Suite (Docs, Meet, etc)	42	1.9%
Github;Gitlab;Google Suite (Docs, Meet, etc)	36	1.6%
Gitlab	35	1.5%
Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)	29	1.3%
Other values (392)	1346	59.5%
(Missing)	238	10.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
suite	826	12.2%
docs	826	12.2%
meet	826	12.2%
etc	699	10.3%
teams	352	5.2%
for	240	3.5%
overflow	240	3.5%
teams;microsoft	154	2.3%
github	153	2.3%
azure	140	2.1%
Other values (190)	2308	34.1%

Most occurring characters

Value	Count	Frequency (%)
e	6616	8.8%
t	5837	7.8%
o	5264	7.0%
	4741	6.3%
;	4699	6.3%
i	4602	6.1%
l	4153	5.5%
c	4081	5.4%
a	3382	4.5%
G	3275	4.4%
Other values (27)	28465	37.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	51974	69.2%
Uppercase Letter	10397	13.8%
Other Punctuation	6351	8.5%
Space Separator	4741	6.3%
Open Punctuation	826	1.1%
Close Punctuation	826	1.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	6616	12.7%
t	5837	11.2%
o	5264	10.1%
i	4602	8.9%
l	4153	8.0%
c	4081	7.9%
a	3382	6.5%
u	3219	6.2%
r	2588	5.0%
b	2496	4.8%
Other values (11)	9736	18.7%

Uppercase Letter

Value	Count	Frequency (%)
G	3275	31.5%
S	2074	19.9%
M	1496	14.4%
T	992	9.5%
D	826	7.9%
J	657	6.3%
C	417	4.0%
A	326	3.1%
O	240	2.3%
F	47	0.5%

Other Punctuation

Value	Count	Frequency (%)
;	4699	74.0%
,	1652	26.0%

Space Separator

Value	Count	Frequency (%)
	4741	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	826	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	826	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	62371	83.0%
Common	12744	17.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	6616	10.6%
t	5837	9.4%
o	5264	8.4%
i	4602	7.4%
l	4153	6.7%
c	4081	6.5%
a	3382	5.4%
G	3275	5.3%
u	3219	5.2%
r	2588	4.1%
Other values (22)	19354	31.0%

Common

Value	Count	Frequency (%)
	4741	37.2%
;	4699	36.9%
,	1652	13.0%
(	826	6.5%
)	826	6.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	75115	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	6616	8.8%
t	5837	7.8%
o	5264	7.0%
	4741	6.3%
;	4699	6.3%
i	4602	6.1%
l	4153	5.5%
c	4081	5.4%
a	3382	4.5%
G	3275	4.4%
Other values (27)	28465	37.9%

new_collab_tools_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct	406
Distinct (%)	18.5%
Missing	69
Missing (%)	3.1%
Memory size	17.8 KiB

Github	96
Github;Slack;Google Suite (Docs, Meet, etc)	76
Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)	71
Github;Gitlab	53
Github;Google Suite (Docs, Meet, etc)	49
Other values (401)	1847

Length

Max length	149
Median length	103
Mean length	45.07664234
Min length	4

Characters and Unicode

Total characters	98808
Distinct characters	37
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	170 ?
Unique (%)	7.8%

Sample

1st row	Confluence;Jira;Github;Slack;Trello
2nd row	Confluence;Jira;Github;Gitlab;Microsoft Azure;Google Suite (Docs, Meet, etc)
3rd row	Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)
4th row	Jira
5th row	Github;Slack

Common Values

Value	Count	Frequency (%)
Github	96	4.2%
Github;Slack;Google Suite (Docs, Meet, etc)	76	3.4%
Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)	71	3.1%
Github;Gitlab	53	2.3%
Github;Google Suite (Docs, Meet, etc)	49	2.2%
Github;Gitlab;Slack;Google Suite (Docs, Meet, etc)	47	2.1%
Github;Slack	43	1.9%
Github;Slack;Trello;Google Suite (Docs, Meet, etc)	42	1.9%
Confluence;Jira;Github;Gitlab;Slack;Google Suite (Docs, Meet, etc)	41	1.8%
Github;Gitlab;Slack;Trello;Google Suite (Docs, Meet, etc)	37	1.6%
Other values (396)	1637	72.4%
(Missing)	69	3.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
suite	1115	13.9%
docs	1115	13.9%
meet	1115	13.9%
etc	1037	12.9%
teams	298	3.7%
teams;microsoft	223	2.8%
azure	134	1.7%
overflow	114	1.4%
for	114	1.4%
teams;google	110	1.4%
Other values (178)	2674	33.2%

Most occurring characters

Value	Count	Frequency (%)
e	9177	9.3%
t	7243	7.3%
o	7210	7.3%
;	6785	6.9%
i	5995	6.1%
	5857	5.9%
l	5656	5.7%
c	5605	5.7%
a	4344	4.4%
u	4158	4.2%
Other values (27)	36778	37.2%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	68101	68.9%
Uppercase Letter	13605	13.8%
Other Punctuation	9015	9.1%
Space Separator	5857	5.9%
Open Punctuation	1115	1.1%
Close Punctuation	1115	1.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	9177	13.5%
t	7243	10.6%
o	7210	10.6%
i	5995	8.8%
l	5656	8.3%
c	5605	8.2%
a	4344	6.4%
u	4158	6.1%
r	3387	5.0%
b	2875	4.2%
Other values (11)	12451	18.3%

Uppercase Letter

Value	Count	Frequency (%)
G	3917	28.8%
S	2570	18.9%
M	2097	15.4%
T	1412	10.4%
D	1115	8.2%
J	1096	8.1%
C	792	5.8%
A	346	2.5%
O	114	0.8%
F	73	0.5%

Other Punctuation

Value	Count	Frequency (%)
;	6785	75.3%
,	2230	24.7%

Space Separator

Value	Count	Frequency (%)
	5857	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1115	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1115	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	81706	82.7%
Common	17102	17.3%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	9177	11.2%
t	7243	8.9%
o	7210	8.8%
i	5995	7.3%
l	5656	6.9%
c	5605	6.9%
a	4344	5.3%
u	4158	5.1%
G	3917	4.8%
r	3387	4.1%
Other values (22)	25014	30.6%

Common

Value	Count	Frequency (%)
;	6785	39.7%
	5857	34.2%
,	2230	13.0%
(	1115	6.5%
)	1115	6.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	98808	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	9177	9.3%
t	7243	7.3%
o	7210	7.3%
;	6785	6.9%
i	5995	6.1%
	5857	5.9%
l	5656	5.7%
c	5605	5.7%
a	4344	4.4%
u	4158	4.2%
Other values (27)	36778	37.2%

new_dev_ops
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	16
Missing (%)	0.7%
Memory size	17.8 KiB

Yes	973
No	917
Not sure	355

Length

Max length	8
Median length	3
Mean length	3.382182628
Min length	2

Characters and Unicode

Total characters	7593
Distinct characters	9
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Not sure
2nd row	Yes
3rd row	Yes
4th row	Not sure
5th row	No

Common Values

Value	Count	Frequency (%)
Yes	973	43.0%
No	917	40.6%
Not sure	355	15.7%
(Missing)	16	0.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
yes	973	37.4%
no	917	35.3%
not	355	13.7%
sure	355	13.7%

Most occurring characters

Value	Count	Frequency (%)
e	1328	17.5%
s	1328	17.5%
N	1272	16.8%
o	1272	16.8%
Y	973	12.8%
t	355	4.7%
	355	4.7%
u	355	4.7%
r	355	4.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	4993	65.8%
Uppercase Letter	2245	29.6%
Space Separator	355	4.7%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	1328	26.6%
s	1328	26.6%
o	1272	25.5%
t	355	7.1%
u	355	7.1%
r	355	7.1%

Uppercase Letter

Value	Count	Frequency (%)
N	1272	56.7%
Y	973	43.3%

Space Separator

Value	Count	Frequency (%)
	355	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	7238	95.3%
Common	355	4.7%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	1328	18.3%
s	1328	18.3%
N	1272	17.6%
o	1272	17.6%
Y	973	13.4%
t	355	4.9%
u	355	4.9%
r	355	4.9%

Common

Value	Count	Frequency (%)
	355	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	7593	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	1328	17.5%
s	1328	17.5%
N	1272	16.8%
o	1272	16.8%
Y	973	12.8%
t	355	4.7%
	355	4.7%
u	355	4.7%
r	355	4.7%

new_dev_ops_impt
Categorical

MISSING

Distinct	5
Distinct (%)	0.2%
Missing	78
Missing (%)	3.4%
Memory size	17.8 KiB

Extremely important	1038
Somewhat important	662
Neutral	406
Not very important	47
Not at all important	30

Length

Max length	20
Median length	19
Mean length	16.45716903
Min length	7

Characters and Unicode

Total characters	35926
Distinct characters	20
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Neutral
2nd row	Neutral
3rd row	Extremely important
4th row	Neutral
5th row	Somewhat important

Common Values

Value	Count	Frequency (%)
Extremely important	1038	45.9%
Somewhat important	662	29.3%
Neutral	406	18.0%
Not very important	47	2.1%
Not at all important	30	1.3%
(Missing)	78	3.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	1777	43.7%
extremely	1038	25.5%
somewhat	662	16.3%
neutral	406	10.0%
not	77	1.9%
very	47	1.2%
at	30	0.7%
all	30	0.7%

Most occurring characters

Value	Count	Frequency (%)
t	5767	16.1%
m	3477	9.7%
r	3268	9.1%
e	3191	8.9%
a	2905	8.1%
o	2516	7.0%
	1884	5.2%
p	1777	4.9%
i	1777	4.9%
n	1777	4.9%
Other values (10)	7587	21.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	31859	88.7%
Uppercase Letter	2183	6.1%
Space Separator	1884	5.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	5767	18.1%
m	3477	10.9%
r	3268	10.3%
e	3191	10.0%
a	2905	9.1%
o	2516	7.9%
p	1777	5.6%
i	1777	5.6%
n	1777	5.6%
l	1504	4.7%
Other values (6)	3900	12.2%

Uppercase Letter

Value	Count	Frequency (%)
E	1038	47.5%
S	662	30.3%
N	483	22.1%

Space Separator

Value	Count	Frequency (%)
	1884	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	34042	94.8%
Common	1884	5.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	5767	16.9%
m	3477	10.2%
r	3268	9.6%
e	3191	9.4%
a	2905	8.5%
o	2516	7.4%
p	1777	5.2%
i	1777	5.2%
n	1777	5.2%
l	1504	4.4%
Other values (9)	6083	17.9%

Common

Value	Count	Frequency (%)
	1884	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	35926	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	5767	16.1%
m	3477	9.7%
r	3268	9.1%
e	3191	8.9%
a	2905	8.1%
o	2516	7.0%
	1884	5.2%
p	1777	4.9%
i	1777	4.9%
n	1777	4.9%
Other values (10)	7587	21.1%

new_ed_impt
Categorical

Distinct	5
Distinct (%)	0.2%
Missing	15
Missing (%)	0.7%
Memory size	17.8 KiB

Very important	663
Fairly important	550
Critically important	467
Somewhat important	360
Not at all important/not necessary	206

Length

Max length	34
Median length	20
Mean length	18.2128228
Min length	14

Characters and Unicode

Total characters	40906
Distinct characters	22
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Somewhat important
2nd row	Not at all important/not necessary
3rd row	Very important
4th row	Critically important
5th row	Very important

Common Values

Value	Count	Frequency (%)
Very important	663	29.3%
Fairly important	550	24.3%
Critically important	467	20.7%
Somewhat important	360	15.9%
Not at all important/not necessary	206	9.1%
(Missing)	15	0.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
important	2040	39.9%
very	663	13.0%
fairly	550	10.8%
critically	467	9.1%
somewhat	360	7.0%
not	206	4.0%
at	206	4.0%
all	206	4.0%
important/not	206	4.0%
necessary	206	4.0%

Most occurring characters

Value	Count	Frequency (%)
t	5937	14.5%
a	4241	10.4%
r	4132	10.1%
i	3730	9.1%
o	3018	7.4%
	2864	7.0%
n	2658	6.5%
m	2606	6.4%
p	2246	5.5%
l	1896	4.6%
Other values (12)	7578	18.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	35590	87.0%
Space Separator	2864	7.0%
Uppercase Letter	2246	5.5%
Other Punctuation	206	0.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
t	5937	16.7%
a	4241	11.9%
r	4132	11.6%
i	3730	10.5%
o	3018	8.5%
n	2658	7.5%
m	2606	7.3%
p	2246	6.3%
l	1896	5.3%
y	1886	5.3%
Other values (5)	3240	9.1%

Uppercase Letter

Value	Count	Frequency (%)
V	663	29.5%
F	550	24.5%
C	467	20.8%
S	360	16.0%
N	206	9.2%

Space Separator

Value	Count	Frequency (%)
	2864	100.0%

Other Punctuation

Value	Count	Frequency (%)
/	206	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	37836	92.5%
Common	3070	7.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
t	5937	15.7%
a	4241	11.2%
r	4132	10.9%
i	3730	9.9%
o	3018	8.0%
n	2658	7.0%
m	2606	6.9%
p	2246	5.9%
l	1896	5.0%
y	1886	5.0%
Other values (10)	5486	14.5%

Common

Value	Count	Frequency (%)
	2864	93.3%
/	206	6.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	40906	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
t	5937	14.5%
a	4241	10.4%
r	4132	10.1%
i	3730	9.1%
o	3018	7.4%
	2864	7.0%
n	2658	6.5%
m	2606	6.4%
p	2246	5.5%
l	1896	4.6%
Other values (12)	7578	18.5%

new_job_hunt
Categorical

HIGH CARDINALITY
MISSING

Distinct	697
Distinct (%)	31.8%
Missing	70
Missing (%)	3.1%
Memory size	17.8 KiB

Curious about other opportunities;Better compensation;Wanting to work with new technologies;Growth or leadership opportunities	60
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities	50
Curious about other opportunities;Better compensation;Growth or leadership opportunities	45
Curious about other opportunities	40
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate	39
Other values (692)	1957

Length

Max length	377
Median length	257
Mean length	130.9566408
Min length	12

Characters and Unicode

Total characters	286926
Distinct characters	35
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	393 ?
Unique (%)	17.9%

Sample

1st row	Having a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities
2nd row	Just because;Having a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Looking to relocate
3rd row	Curious about other opportunities;Better compensation;Trouble with leadership at my company;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate
4th row	Better work/life balance
5th row	Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities

Common Values

Value	Count	Frequency (%)
Curious about other opportunities;Better compensation;Wanting to work with new technologies;Growth or leadership opportunities	60	2.7%
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities	50	2.2%
Curious about other opportunities;Better compensation;Growth or leadership opportunities	45	2.0%
Curious about other opportunities	40	1.8%
Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate	39	1.7%
Curious about other opportunities;Better compensation;Wanting to work with new technologies	38	1.7%
Better compensation	31	1.4%
Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities	28	1.2%
Better compensation;Wanting to work with new technologies;Growth or leadership opportunities	28	1.2%
Curious about other opportunities;Better compensation	26	1.1%
Other values (687)	1806	79.9%
(Missing)	70	3.1%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
with	2697	8.2%
or	2254	6.9%
to	2143	6.5%
leadership	1917	5.8%
about	1412	4.3%
other	1412	4.3%
work	1257	3.8%
new	1249	3.8%
my	1184	3.6%
opportunities;better	1129	3.4%
Other values (72)	16233	49.4%

Most occurring characters

Value	Count	Frequency (%)
	30696	10.7%
o	28424	9.9%
t	27738	9.7%
e	26656	9.3%
r	19651	6.8%
i	18808	6.6%
n	15416	5.4%
a	14811	5.2%
s	10570	3.7%
p	9817	3.4%
Other values (25)	84339	29.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	237684	82.8%
Space Separator	30696	10.7%
Uppercase Letter	9413	3.3%
Other Punctuation	8209	2.9%
Open Punctuation	462	0.2%
Close Punctuation	462	0.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	28424	12.0%
t	27738	11.7%
e	26656	11.2%
r	19651	8.3%
i	18808	7.9%
n	15416	6.5%
a	14811	6.2%
s	10570	4.4%
p	9817	4.1%
h	9595	4.0%
Other values (12)	56198	23.6%

Uppercase Letter

Value	Count	Frequency (%)
B	2552	27.1%
W	1513	16.1%
C	1412	15.0%
G	1330	14.1%
T	1184	12.6%
L	630	6.7%
H	462	4.9%
J	330	3.5%

Other Punctuation

Value	Count	Frequency (%)
;	7222	88.0%
/	987	12.0%

Space Separator

Value	Count	Frequency (%)
	30696	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	462	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	462	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	247097	86.1%
Common	39829	13.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	28424	11.5%
t	27738	11.2%
e	26656	10.8%
r	19651	8.0%
i	18808	7.6%
n	15416	6.2%
a	14811	6.0%
s	10570	4.3%
p	9817	4.0%
h	9595	3.9%
Other values (20)	65611	26.6%

Common

Value	Count	Frequency (%)
	30696	77.1%
;	7222	18.1%
/	987	2.5%
(	462	1.2%
)	462	1.2%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	286926	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	30696	10.7%
o	28424	9.9%
t	27738	9.7%
e	26656	9.3%
r	19651	6.8%
i	18808	6.6%
n	15416	5.4%
a	14811	5.2%
s	10570	3.7%
p	9817	3.4%
Other values (25)	84339	29.4%

new_job_hunt_research
Categorical

HIGH CARDINALITY
MISSING

Distinct	62
Distinct (%)	2.9%
Missing	112
Missing (%)	5.0%
Memory size	17.8 KiB

Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	189
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family	134
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Read other media like news articles, founder profiles, etc. about the company	101
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	92
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	88
Other values (57)	1545

Length

Max length	362
Median length	247
Mean length	199.6351792
Min length	36

Characters and Unicode

Total characters	429016
Distinct characters	35
Distinct categories	7 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	2 ?
Unique (%)	0.1%

Sample

1st row	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company
2nd row	Read company media, such as employee blogs or company culture videos;Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company
3rd row	Personal network - friends or family;Directly asking current or past employees at the company
4th row	Read company media, such as employee blogs or company culture videos;Personal network - friends or family
5th row	Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company

Common Values

Value	Count	Frequency (%)
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	189	8.4%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family	134	5.9%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Read other media like news articles, founder profiles, etc. about the company	101	4.5%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	92	4.1%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	88	3.9%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company	76	3.4%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	76	3.4%
Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company	75	3.3%
Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind)	73	3.2%
Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	66	2.9%
Other values (52)	1179	52.1%
(Missing)	112	5.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
company	4887	8.2%
or	3804	6.4%
media	2584	4.4%
e.g	2195	3.7%
the	2091	3.5%
party	1522	2.6%
sites	1522	2.6%
glassdoor	1522	2.6%
reviews	1522	2.6%
from	1522	2.6%
Other values (48)	36230	61.0%

Most occurring characters

Value	Count	Frequency (%)
	57252	13.3%
e	39626	9.2%
o	28930	6.7%
a	28819	6.7%
r	26000	6.1%
s	23150	5.4%
i	22398	5.2%
n	19901	4.6%
l	18933	4.4%
t	18573	4.3%
Other values (25)	145434	33.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	339187	79.1%
Space Separator	57252	13.3%
Other Punctuation	15844	3.7%
Uppercase Letter	10882	2.5%
Open Punctuation	2195	0.5%
Close Punctuation	2195	0.5%
Dash Punctuation	1461	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	39626	11.7%
o	28930	8.5%
a	28819	8.5%
r	26000	7.7%
s	23150	6.8%
i	22398	6.6%
n	19901	5.9%
l	18933	5.6%
t	18573	5.5%
m	15032	4.4%
Other values (12)	97825	28.8%

Uppercase Letter

Value	Count	Frequency (%)
R	2584	23.7%
C	2195	20.2%
P	2134	19.6%
G	1522	14.0%
B	1522	14.0%
D	925	8.5%

Other Punctuation

Value	Count	Frequency (%)
.	5556	35.1%
,	5272	33.3%
;	5016	31.7%

Space Separator

Value	Count	Frequency (%)
	57252	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	2195	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	2195	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	1461	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	350069	81.6%
Common	78947	18.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	39626	11.3%
o	28930	8.3%
a	28819	8.2%
r	26000	7.4%
s	23150	6.6%
i	22398	6.4%
n	19901	5.7%
l	18933	5.4%
t	18573	5.3%
m	15032	4.3%
Other values (18)	108707	31.1%

Common

Value	Count	Frequency (%)
	57252	72.5%
.	5556	7.0%
,	5272	6.7%
;	5016	6.4%
(	2195	2.8%
)	2195	2.8%
-	1461	1.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	429016	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	57252	13.3%
e	39626	9.2%
o	28930	6.7%
a	28819	6.7%
r	26000	6.1%
s	23150	5.4%
i	22398	5.2%
n	19901	4.6%
l	18933	4.4%
t	18573	4.3%
Other values (25)	145434	33.9%

new_learn
Categorical

MISSING

Distinct	4
Distinct (%)	0.2%
Missing	38
Missing (%)	1.7%
Memory size	17.8 KiB

Every few months	847
Once a year	811
Once every few years	540
Once a decade	25

Length

Max length	20
Median length	16
Mean length	15.11381017
Min length	11

Characters and Unicode

Total characters	33598
Distinct characters	18
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Every few months
2nd row	Once a year
3rd row	Once a year
4th row	Once every few years
5th row	Once a year

Common Values

Value	Count	Frequency (%)
Every few months	847	37.5%
Once a year	811	35.9%
Once every few years	540	23.9%
Once a decade	25	1.1%
(Missing)	38	1.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
every	1387	19.2%
few	1387	19.2%
once	1376	19.1%
months	847	11.7%
a	836	11.6%
year	811	11.2%
years	540	7.5%
decade	25	0.3%

Most occurring characters

Value	Count	Frequency (%)
e	6091	18.1%
	4986	14.8%
r	2738	8.1%
y	2738	8.1%
n	2223	6.6%
a	2212	6.6%
c	1401	4.2%
s	1387	4.1%
f	1387	4.1%
w	1387	4.1%
Other values (8)	7048	21.0%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	26389	78.5%
Space Separator	4986	14.8%
Uppercase Letter	2223	6.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	6091	23.1%
r	2738	10.4%
y	2738	10.4%
n	2223	8.4%
a	2212	8.4%
c	1401	5.3%
s	1387	5.3%
f	1387	5.3%
w	1387	5.3%
v	1387	5.3%
Other values (5)	3438	13.0%

Uppercase Letter

Value	Count	Frequency (%)
O	1376	61.9%
E	847	38.1%

Space Separator

Value	Count	Frequency (%)
	4986	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28612	85.2%
Common	4986	14.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	6091	21.3%
r	2738	9.6%
y	2738	9.6%
n	2223	7.8%
a	2212	7.7%
c	1401	4.9%
s	1387	4.8%
f	1387	4.8%
w	1387	4.8%
v	1387	4.8%
Other values (7)	5661	19.8%

Common

Value	Count	Frequency (%)
	4986	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	33598	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	6091	18.1%
	4986	14.8%
r	2738	8.1%
y	2738	8.1%
n	2223	6.6%
a	2212	6.6%
c	1401	4.2%
s	1387	4.1%
f	1387	4.1%
w	1387	4.1%
Other values (8)	7048	21.0%

new_off_topic
Categorical

MISSING

Distinct	3
Distinct (%)	0.1%
Missing	106
Missing (%)	4.7%
Memory size	17.8 KiB

No	877
Not sure	813
Yes	465

Length

Max length	8
Median length	3
Mean length	4.479350348
Min length	2

Characters and Unicode

Total characters	9653
Distinct characters	9
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	No
2nd row	No
3rd row	No
4th row	No
5th row	No

Common Values

Value	Count	Frequency (%)
No	877	38.8%
Not sure	813	36.0%
Yes	465	20.6%
(Missing)	106	4.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
no	877	29.5%
not	813	27.4%
sure	813	27.4%
yes	465	15.7%

Most occurring characters

Value	Count	Frequency (%)
N	1690	17.5%
o	1690	17.5%
s	1278	13.2%
e	1278	13.2%
t	813	8.4%
	813	8.4%
u	813	8.4%
r	813	8.4%
Y	465	4.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6685	69.3%
Uppercase Letter	2155	22.3%
Space Separator	813	8.4%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	1690	25.3%
s	1278	19.1%
e	1278	19.1%
t	813	12.2%
u	813	12.2%
r	813	12.2%

Uppercase Letter

Value	Count	Frequency (%)
N	1690	78.4%
Y	465	21.6%

Space Separator

Value	Count	Frequency (%)
	813	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8840	91.6%
Common	813	8.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
N	1690	19.1%
o	1690	19.1%
s	1278	14.5%
e	1278	14.5%
t	813	9.2%
u	813	9.2%
r	813	9.2%
Y	465	5.3%

Common

Value	Count	Frequency (%)
	813	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9653	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
N	1690	17.5%
o	1690	17.5%
s	1278	13.2%
e	1278	13.2%
t	813	8.4%
	813	8.4%
u	813	8.4%
r	813	8.4%
Y	465	4.8%

new_onboard_good
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	17
Missing (%)	0.8%
Memory size	17.8 KiB

Yes	1062
No	653
Onboarding? What onboarding?	529

Length

Max length	28
Median length	3
Mean length	8.602495544
Min length	2

Characters and Unicode

Total characters	19304
Distinct characters	18
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	Onboarding? What onboarding?
3rd row	No
4th row	Yes
5th row	Onboarding? What onboarding?

Common Values

Value	Count	Frequency (%)
Yes	1062	47.0%
No	653	28.9%
Onboarding? What onboarding?	529	23.4%
(Missing)	17	0.8%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
yes	1062	32.2%
onboarding	1058	32.0%
no	653	19.8%
what	529	16.0%

Most occurring characters

Value	Count	Frequency (%)
o	2240	11.6%
n	2116	11.0%
a	1587	8.2%
Y	1062	5.5%
s	1062	5.5%
e	1062	5.5%
i	1058	5.5%
	1058	5.5%
?	1058	5.5%
g	1058	5.5%
Other values (8)	5943	30.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	14415	74.7%
Uppercase Letter	2773	14.4%
Space Separator	1058	5.5%
Other Punctuation	1058	5.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	2240	15.5%
n	2116	14.7%
a	1587	11.0%
s	1062	7.4%
e	1062	7.4%
i	1058	7.3%
g	1058	7.3%
r	1058	7.3%
d	1058	7.3%
b	1058	7.3%
Other values (2)	1058	7.3%

Uppercase Letter

Value	Count	Frequency (%)
Y	1062	38.3%
N	653	23.5%
O	529	19.1%
W	529	19.1%

Space Separator

Value	Count	Frequency (%)
	1058	100.0%

Other Punctuation

Value	Count	Frequency (%)
?	1058	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	17188	89.0%
Common	2116	11.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	2240	13.0%
n	2116	12.3%
a	1587	9.2%
Y	1062	6.2%
s	1062	6.2%
e	1062	6.2%
i	1058	6.2%
g	1058	6.2%
r	1058	6.2%
d	1058	6.2%
Other values (6)	3827	22.3%

Common

Value	Count	Frequency (%)
	1058	50.0%
?	1058	50.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	19304	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	2240	11.6%
n	2116	11.0%
a	1587	8.2%
Y	1062	5.5%
s	1062	5.5%
e	1062	5.5%
i	1058	5.5%
	1058	5.5%
?	1058	5.5%
g	1058	5.5%
Other values (8)	5943	30.8%

new_other_comms
Boolean

Distinct	2
Distinct (%)	0.1%
Missing	4
Missing (%)	0.2%
Memory size	4.5 KiB

False	1312
True	945
(Missing)	4

Common Values
Category Frequency Plot

Value	Count	Frequency (%)
False	1312	58.0%
True	945	41.8%
(Missing)	4	0.2%

new_overtime
Categorical

Distinct	5
Distinct (%)	0.2%
Missing	6
Missing (%)	0.3%
Memory size	17.8 KiB

Often: 1-2 days per week or more	706
Sometimes: 1-2 days per month but less than weekly	642
Occasionally: 1-2 days per quarter but less than monthly	413
Rarely: 1-2 days per year or less	290
Never	204

Length

Max length	56
Median length	50
Mean length	39.20620843
Min length	5

Characters and Unicode

Total characters	88410
Distinct characters	31
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Sometimes: 1-2 days per month but less than weekly
2nd row	Occasionally: 1-2 days per quarter but less than monthly
3rd row	Sometimes: 1-2 days per month but less than weekly
4th row	Occasionally: 1-2 days per quarter but less than monthly
5th row	Rarely: 1-2 days per year or less

Common Values

Value	Count	Frequency (%)
Often: 1-2 days per week or more	706	31.2%
Sometimes: 1-2 days per month but less than weekly	642	28.4%
Occasionally: 1-2 days per quarter but less than monthly	413	18.3%
Rarely: 1-2 days per year or less	290	12.8%
Never	204	9.0%
(Missing)	6	0.3%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
1-2	2051	12.3%
days	2051	12.3%
per	2051	12.3%
less	1345	8.1%
but	1055	6.3%
than	1055	6.3%
or	996	6.0%
often	706	4.2%
more	706	4.2%
week	706	4.2%
Other values (9)	3949	23.7%

Most occurring characters

Value	Count	Frequency (%)
	14416	16.3%
e	10189	11.5%
s	5796	6.6%
r	5363	6.1%
t	4926	5.6%
a	4925	5.6%
y	4099	4.6%
o	3812	4.3%
l	3516	4.0%
n	3229	3.7%
Other values (21)	28139	31.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	63535	71.9%
Space Separator	14416	16.3%
Decimal Number	4102	4.6%
Uppercase Letter	2255	2.6%
Dash Punctuation	2051	2.3%
Other Punctuation	2051	2.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	10189	16.0%
s	5796	9.1%
r	5363	8.4%
t	4926	7.8%
a	4925	7.8%
y	4099	6.5%
o	3812	6.0%
l	3516	5.5%
n	3229	5.1%
m	3045	4.8%
Other values (12)	14635	23.0%

Uppercase Letter

Value	Count	Frequency (%)
O	1119	49.6%
S	642	28.5%
R	290	12.9%
N	204	9.0%

Decimal Number

Value	Count	Frequency (%)
2	2051	50.0%
1	2051	50.0%

Space Separator

Value	Count	Frequency (%)
	14416	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	2051	100.0%

Other Punctuation

Value	Count	Frequency (%)
:	2051	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	65790	74.4%
Common	22620	25.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	10189	15.5%
s	5796	8.8%
r	5363	8.2%
t	4926	7.5%
a	4925	7.5%
y	4099	6.2%
o	3812	5.8%
l	3516	5.3%
n	3229	4.9%
m	3045	4.6%
Other values (16)	16890	25.7%

Common

Value	Count	Frequency (%)
	14416	63.7%
2	2051	9.1%
-	2051	9.1%
1	2051	9.1%
:	2051	9.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	88410	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	14416	16.3%
e	10189	11.5%
s	5796	6.6%
r	5363	6.1%
t	4926	5.6%
a	4925	5.6%
y	4099	4.6%
o	3812	4.3%
l	3516	4.0%
n	3229	3.7%
Other values (21)	28139	31.8%

new_purchase_research
Categorical

HIGH CARDINALITY
HIGH CORRELATION
MISSING

Distinct	56
Distinct (%)	3.8%
Missing	781
Missing (%)	34.5%
Memory size	17.8 KiB

Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow	321
Start a free trial;Ask developers I know/work with	188
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	169
Start a free trial;Visit developer communities like Stack Overflow	100
Ask developers I know/work with;Visit developer communities like Stack Overflow	98
Other values (51)	604

Length

Max length	253
Median length	213
Mean length	101.072973
Min length	18

Characters and Unicode

Total characters	149588
Distinct characters	33
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	12 ?
Unique (%)	0.8%

Sample

1st row	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow
2nd row	Ask developers I know/work with;Visit developer communities like Stack Overflow
3rd row	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow
4th row	Start a free trial;Ask developers I know/work with
5th row	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow

Common Values

Value	Count	Frequency (%)
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow	321	14.2%
Start a free trial;Ask developers I know/work with	188	8.3%
Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	169	7.5%
Start a free trial;Visit developer communities like Stack Overflow	100	4.4%
Ask developers I know/work with;Visit developer communities like Stack Overflow	98	4.3%
Start a free trial	79	3.5%
Ask developers I know/work with	50	2.2%
Start a free trial;Ask developers I know/work with;Read ratings or reviews on third party sites like G2Crowd	50	2.2%
Start a free trial;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	48	2.1%
Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	41	1.8%
Other values (46)	336	14.9%
(Missing)	781	34.5%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
like	1488	7.0%
i	1342	6.4%
start	1180	5.6%
free	1180	5.6%
a	1180	5.6%
developers	1128	5.3%
know/work	1128	5.3%
developer	1011	4.8%
communities	1011	4.8%
stack	1011	4.8%
Other values (32)	9453	44.8%

Most occurring characters

Value	Count	Frequency (%)
	19632	13.1%
e	16421	11.0%
r	11212	7.5%
i	11026	7.4%
t	10887	7.3%
o	8380	5.6%
s	7678	5.1%
a	7572	5.1%
l	5922	4.0%
k	5883	3.9%
Other values (23)	44975	30.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	117285	78.4%
Space Separator	19632	13.1%
Uppercase Letter	8432	5.6%
Other Punctuation	3762	2.5%
Decimal Number	477	0.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	16421	14.0%
r	11212	9.6%
i	11026	9.4%
t	10887	9.3%
o	8380	7.1%
s	7678	6.5%
a	7572	6.5%
l	5922	5.0%
k	5883	5.0%
w	5349	4.6%
Other values (11)	26955	23.0%

Uppercase Letter

Value	Count	Frequency (%)
S	2191	26.0%
I	1342	15.9%
A	1128	13.4%
V	1011	12.0%
O	1011	12.0%
R	795	9.4%
G	477	5.7%
C	477	5.7%

Other Punctuation

Value	Count	Frequency (%)
;	2634	70.0%
/	1128	30.0%

Space Separator

Value	Count	Frequency (%)
	19632	100.0%

Decimal Number

Value	Count	Frequency (%)
2	477	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	125717	84.0%
Common	23871	16.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	16421	13.1%
r	11212	8.9%
i	11026	8.8%
t	10887	8.7%
o	8380	6.7%
s	7678	6.1%
a	7572	6.0%
l	5922	4.7%
k	5883	4.7%
w	5349	4.3%
Other values (19)	35387	28.1%

Common

Value	Count	Frequency (%)
	19632	82.2%
;	2634	11.0%
/	1128	4.7%
2	477	2.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	149588	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	19632	13.1%
e	16421	11.0%
r	11212	7.5%
i	11026	7.4%
t	10887	7.3%
o	8380	5.6%
s	7678	5.1%
a	7572	5.1%
l	5922	4.0%
k	5883	3.9%
Other values (23)	44975	30.1%

purple_link
Categorical

Distinct	4
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

Hello, old friend	1225
Indifferent	405
Amused	368
Annoyed	263

Length

Max length	17
Median length	17
Mean length	12.97169394
Min length	6

Characters and Unicode

Total characters	29329
Distinct characters	18
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Hello, old friend
2nd row	Hello, old friend
3rd row	Hello, old friend
4th row	Amused
5th row	Hello, old friend

Common Values

Value	Count	Frequency (%)
Hello, old friend	1225	54.2%
Indifferent	405	17.9%
Amused	368	16.3%
Annoyed	263	11.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
hello	1225	26.0%
old	1225	26.0%
friend	1225	26.0%
indifferent	405	8.6%
amused	368	7.8%
annoyed	263	5.6%

Most occurring characters

Value	Count	Frequency (%)
e	3891	13.3%
l	3675	12.5%
d	3486	11.9%
o	2713	9.3%
n	2561	8.7%
	2450	8.4%
f	2035	6.9%
i	1630	5.6%
r	1630	5.6%
H	1225	4.2%
Other values (8)	4033	13.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	23393	79.8%
Space Separator	2450	8.4%
Uppercase Letter	2261	7.7%
Other Punctuation	1225	4.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3891	16.6%
l	3675	15.7%
d	3486	14.9%
o	2713	11.6%
n	2561	10.9%
f	2035	8.7%
i	1630	7.0%
r	1630	7.0%
t	405	1.7%
m	368	1.6%
Other values (3)	999	4.3%

Uppercase Letter

Value	Count	Frequency (%)
H	1225	54.2%
A	631	27.9%
I	405	17.9%

Space Separator

Value	Count	Frequency (%)
	2450	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	1225	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	25654	87.5%
Common	3675	12.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3891	15.2%
l	3675	14.3%
d	3486	13.6%
o	2713	10.6%
n	2561	10.0%
f	2035	7.9%
i	1630	6.4%
r	1630	6.4%
H	1225	4.8%
A	631	2.5%
Other values (6)	2177	8.5%

Common

Value	Count	Frequency (%)
	2450	66.7%
,	1225	33.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	29329	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	3891	13.3%
l	3675	12.5%
d	3486	11.9%
o	2713	9.3%
n	2561	8.7%
	2450	8.4%
f	2035	6.9%
i	1630	5.6%
r	1630	5.6%
H	1225	4.2%
Other values (8)	4033	13.8%

newso_sites
Categorical

HIGH CORRELATION

Distinct	25
Distinct (%)	1.1%
Missing	4
Missing (%)	0.2%
Memory size	17.8 KiB

Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	1021
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)	667
Stack Overflow (public Q&A for anyone who codes)	303
Stack Overflow (public Q&A for anyone who codes);Stack Overflow Jobs (for job seekers)	122
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)	36
Other values (20)	108

Length

Max length	305
Median length	251
Mean length	108.6672574
Min length	37

Characters and Unicode

Total characters	245262
Distinct characters	39
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	7 ?
Unique (%)	0.3%

Sample

1st row	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
2nd row	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)
3rd row	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
4th row	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)
5th row	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)

Common Values

Value	Count	Frequency (%)
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	1021	45.2%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)	667	29.5%
Stack Overflow (public Q&A for anyone who codes)	303	13.4%
Stack Overflow (public Q&A for anyone who codes);Stack Overflow Jobs (for job seekers)	122	5.4%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)	36	1.6%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow for Teams (private Q&A for organizations)	22	1.0%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow Talent (for hiring companies/recruiters)	18	0.8%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations);Stack Overflow Talent (for hiring companies/recruiters);Stack Overflow Advertising (for technology companies)	12	0.5%
Stack Overflow (public Q&A for anyone who codes);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)	8	0.4%
Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Advertising (for technology companies)	7	0.3%
Other values (15)	41	1.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
for	5204	13.7%
q&a	4143	10.9%
public	4050	10.7%
overflow	3309	8.7%
stack	2253	5.9%
anyone	2248	5.9%
who	2248	5.9%
codes);stack	1945	5.1%
of	1806	4.8%
variety	1802	4.8%
Other values (27)	8920	23.5%

Most occurring characters

Value	Count	Frequency (%)
	35671	14.5%
o	20963	8.5%
c	15177	6.2%
e	14580	5.9%
a	13280	5.4%
r	11632	4.7%
f	10319	4.2%
t	9077	3.7%
i	8244	3.4%
l	7441	3.0%
Other values (29)	98878	40.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	172742	70.4%
Space Separator	35671	14.5%
Uppercase Letter	19573	8.0%
Other Punctuation	7054	2.9%
Open Punctuation	5111	2.1%
Close Punctuation	5111	2.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	20963	12.1%
c	15177	8.8%
e	14580	8.4%
a	13280	7.7%
r	11632	6.7%
f	10319	6.0%
t	9077	5.3%
i	8244	4.8%
l	7441	4.3%
s	7074	4.1%
Other values (15)	54955	31.8%

Uppercase Letter

Value	Count	Frequency (%)
S	5111	26.1%
A	4172	21.3%
Q	4143	21.2%
O	3309	16.9%
E	1802	9.2%
J	886	4.5%
T	146	0.7%
I	4	< 0.1%

Other Punctuation

Value	Count	Frequency (%)
&	4143	58.7%
;	2858	40.5%
/	53	0.8%

Space Separator

Value	Count	Frequency (%)
	35671	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	5111	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	5111	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	192315	78.4%
Common	52947	21.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	20963	10.9%
c	15177	7.9%
e	14580	7.6%
a	13280	6.9%
r	11632	6.0%
f	10319	5.4%
t	9077	4.7%
i	8244	4.3%
l	7441	3.9%
s	7074	3.7%
Other values (23)	74528	38.8%

Common

Value	Count	Frequency (%)
	35671	67.4%
(	5111	9.7%
)	5111	9.7%
&	4143	7.8%
;	2858	5.4%
/	53	0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	245262	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	35671	14.5%
o	20963	8.5%
c	15177	6.2%
e	14580	5.9%
a	13280	5.4%
r	11632	4.7%
f	10319	4.2%
t	9077	3.7%
i	8244	3.4%
l	7441	3.0%
Other values (29)	98878	40.3%

new_stuck
Categorical

HIGH CARDINALITY
MISSING

Distinct	215
Distinct (%)	9.6%
Missing	29
Missing (%)	1.3%
Memory size	17.8 KiB

Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	107
Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later	106
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later	98
Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos	97
Call a coworker or friend;Visit Stack Overflow;Do other work and come back later	96
Other values (210)	1728

Length

Max length	225
Median length	184
Mean length	98.00224014
Min length	20

Characters and Unicode

Total characters	218741
Distinct characters	37
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	73 ?
Unique (%)	3.3%

Sample

1st row	Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later
2nd row	Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later
3rd row	Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later
4th row	Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Visit another developer community (please name):
5th row	Play games;Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Visit another developer community (please name):

Common Values

Value	Count	Frequency (%)
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	107	4.7%
Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later	106	4.7%
Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later	98	4.3%
Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos	97	4.3%
Call a coworker or friend;Visit Stack Overflow;Do other work and come back later	96	4.2%
Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos;Do other work and come back later	93	4.1%
Visit Stack Overflow;Watch help / tutorial videos	92	4.1%
Visit Stack Overflow;Watch help / tutorial videos;Do other work and come back later	82	3.6%
Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	80	3.5%
Call a coworker or friend;Visit Stack Overflow	74	3.3%
Other values (205)	1307	57.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
other	2502	7.5%
or	2329	7.0%
a	2329	7.0%
stack	2095	6.3%
come	1348	4.0%
work	1348	4.0%
back	1348	4.0%
and	1348	4.0%
later	1213	3.6%
help	1181	3.5%
Other values (57)	16443	49.1%

Most occurring characters

Value	Count	Frequency (%)
	31252	14.3%
o	18701	8.5%
a	17305	7.9%
r	15956	7.3%
t	15232	7.0%
e	14581	6.7%
i	12453	5.7%
l	11224	5.1%
c	9930	4.5%
k	7120	3.3%
Other values (27)	64987	29.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	167579	76.6%
Space Separator	31252	14.3%
Uppercase Letter	12220	5.6%
Other Punctuation	7216	3.3%
Open Punctuation	237	0.1%
Close Punctuation	237	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	18701	11.2%
a	17305	10.3%
r	15956	9.5%
t	15232	9.1%
e	14581	8.7%
i	12453	7.4%
l	11224	6.7%
c	9930	5.9%
k	7120	4.2%
h	6255	3.7%
Other values (12)	38822	23.2%

Uppercase Letter

Value	Count	Frequency (%)
V	2332	19.1%
S	2095	17.1%
O	2095	17.1%
D	1348	11.0%
W	1181	9.7%
C	1175	9.6%
G	1154	9.4%
P	525	4.3%
M	315	2.6%

Other Punctuation

Value	Count	Frequency (%)
;	5798	80.3%
/	1181	16.4%
:	237	3.3%

Space Separator

Value	Count	Frequency (%)
	31252	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	237	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	237	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	179799	82.2%
Common	38942	17.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	18701	10.4%
a	17305	9.6%
r	15956	8.9%
t	15232	8.5%
e	14581	8.1%
i	12453	6.9%
l	11224	6.2%
c	9930	5.5%
k	7120	4.0%
h	6255	3.5%
Other values (21)	51042	28.4%

Common

Value	Count	Frequency (%)
	31252	80.3%
;	5798	14.9%
/	1181	3.0%
(	237	0.6%
)	237	0.6%
:	237	0.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	218741	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	31252	14.3%
o	18701	8.5%
a	17305	7.9%
r	15956	7.3%
t	15232	7.0%
e	14581	6.7%
i	12453	5.7%
l	11224	5.1%
c	9930	4.5%
k	7120	3.3%
Other values (27)	64987	29.7%

op_sys
Categorical

Distinct	4
Distinct (%)	0.2%
Missing	16
Missing (%)	0.7%
Memory size	17.8 KiB

Windows	906
Linux-based	814
MacOS	524
BSD	1

Length

Max length	11
Median length	7
Mean length	7.981737194
Min length	3

Characters and Unicode

Total characters	17919
Distinct characters	20
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	Windows
2nd row	Linux-based
3rd row	MacOS
4th row	Windows
5th row	Windows

Common Values

Value	Count	Frequency (%)
Windows	906	40.1%
Linux-based	814	36.0%
MacOS	524	23.2%
BSD	1	< 0.1%
(Missing)	16	0.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
windows	906	40.4%
linux-based	814	36.3%
macos	524	23.3%
bsd	1	< 0.1%

Most occurring characters

Value	Count	Frequency (%)
i	1720	9.6%
n	1720	9.6%
d	1720	9.6%
s	1720	9.6%
a	1338	7.5%
W	906	5.1%
o	906	5.1%
w	906	5.1%
e	814	4.5%
b	814	4.5%
Other values (10)	5355	29.9%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	13810	77.1%
Uppercase Letter	3295	18.4%
Dash Punctuation	814	4.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
i	1720	12.5%
n	1720	12.5%
d	1720	12.5%
s	1720	12.5%
a	1338	9.7%
o	906	6.6%
w	906	6.6%
e	814	5.9%
b	814	5.9%
x	814	5.9%
Other values (2)	1338	9.7%

Uppercase Letter

Value	Count	Frequency (%)
W	906	27.5%
L	814	24.7%
S	525	15.9%
M	524	15.9%
O	524	15.9%
B	1	< 0.1%
D	1	< 0.1%

Dash Punctuation

Value	Count	Frequency (%)
-	814	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	17105	95.5%
Common	814	4.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
i	1720	10.1%
n	1720	10.1%
d	1720	10.1%
s	1720	10.1%
a	1338	7.8%
W	906	5.3%
o	906	5.3%
w	906	5.3%
e	814	4.8%
b	814	4.8%
Other values (9)	4541	26.5%

Common

Value	Count	Frequency (%)
-	814	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	17919	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
i	1720	9.6%
n	1720	9.6%
d	1720	9.6%
s	1720	9.6%
a	1338	7.5%
W	906	5.1%
o	906	5.1%
w	906	5.1%
e	814	4.5%
b	814	4.5%
Other values (10)	5355	29.9%

org_size
Categorical

HIGH CORRELATION
MISSING

Distinct	9
Distinct (%)	0.4%
Missing	38
Missing (%)	1.7%
Memory size	17.8 KiB

20 to 99 employees	444
10,000 or more employees	400
100 to 499 employees	355
1,000 to 4,999 employees	292
2 to 9 employees	213
Other values (4)	519

Length

Max length	50
Median length	24
Mean length	21.64012596
Min length	16

Characters and Unicode

Total characters	48106
Distinct characters	27
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	1,000 to 4,999 employees
2nd row	10 to 19 employees
3rd row	20 to 99 employees
4th row	5,000 to 9,999 employees
5th row	2 to 9 employees

Common Values

Value	Count	Frequency (%)
20 to 99 employees	444	19.6%
10,000 or more employees	400	17.7%
100 to 499 employees	355	15.7%
1,000 to 4,999 employees	292	12.9%
2 to 9 employees	213	9.4%
10 to 19 employees	160	7.1%
5,000 to 9,999 employees	142	6.3%
500 to 999 employees	138	6.1%
Just me - I am a freelancer, sole proprietor, etc.	79	3.5%
(Missing)	38	1.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
employees	2144	22.9%
to	1744	18.6%
20	444	4.7%
99	444	4.7%
10,000	400	4.3%
or	400	4.3%
more	400	4.3%
100	355	3.8%
499	355	3.8%
1,000	292	3.1%
Other values (19)	2388	25.5%

Most occurring characters

Value	Count	Frequency (%)
e	7385	15.4%
	7143	14.8%
o	4925	10.2%
0	4492	9.3%
9	3829	8.0%
m	2702	5.6%
p	2302	4.8%
l	2302	4.8%
s	2302	4.8%
y	2144	4.5%
Other values (17)	8580	17.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	27949	58.1%
Decimal Number	11272	23.4%
Space Separator	7143	14.8%
Other Punctuation	1505	3.1%
Uppercase Letter	158	0.3%
Dash Punctuation	79	0.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	7385	26.4%
o	4925	17.6%
m	2702	9.7%
p	2302	8.2%
l	2302	8.2%
s	2302	8.2%
y	2144	7.7%
t	1981	7.1%
r	1195	4.3%
a	237	0.8%
Other values (5)	474	1.7%

Decimal Number

Value	Count	Frequency (%)
0	4492	39.9%
9	3829	34.0%
1	1367	12.1%
2	657	5.8%
4	647	5.7%
5	280	2.5%

Other Punctuation

Value	Count	Frequency (%)
,	1426	94.8%
.	79	5.2%

Uppercase Letter

Value	Count	Frequency (%)
J	79	50.0%
I	79	50.0%

Space Separator

Value	Count	Frequency (%)
	7143	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	79	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	28107	58.4%
Common	19999	41.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	7385	26.3%
o	4925	17.5%
m	2702	9.6%
p	2302	8.2%
l	2302	8.2%
s	2302	8.2%
y	2144	7.6%
t	1981	7.0%
r	1195	4.3%
a	237	0.8%
Other values (7)	632	2.2%

Common

Value	Count	Frequency (%)
	7143	35.7%
0	4492	22.5%
9	3829	19.1%
,	1426	7.1%
1	1367	6.8%
2	657	3.3%
4	647	3.2%
5	280	1.4%
-	79	0.4%
.	79	0.4%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	48106	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	7385	15.4%
	7143	14.8%
o	4925	10.2%
0	4492	9.3%
9	3829	8.0%
m	2702	5.6%
p	2302	4.8%
l	2302	4.8%
s	2302	4.8%
y	2144	4.5%
Other values (17)	8580	17.8%

platform_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct	948
Distinct (%)	45.9%
Missing	196
Missing (%)	8.7%
Memory size	17.8 KiB

Linux	100
Docker;Linux	55
Linux;Windows	46
Windows	44
AWS;Docker;Kubernetes;Linux	29
Other values (943)	1791

Length

Max length	177
Median length	111
Mean length	38.56949153
Min length	3

Characters and Unicode

Total characters	79646
Distinct characters	39
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	699 ?
Unique (%)	33.8%

Sample

1st row	Linux;MacOS;Windows
2nd row	Arduino;Docker;Linux;Raspberry Pi
3rd row	Kubernetes;Linux
4th row	Docker;Kubernetes
5th row	Docker;Kubernetes

Common Values

Value	Count	Frequency (%)
Linux	100	4.4%
Docker;Linux	55	2.4%
Linux;Windows	46	2.0%
Windows	44	1.9%
AWS;Docker;Kubernetes;Linux	29	1.3%
Docker;Kubernetes;Linux	27	1.2%
AWS;Docker;Linux	24	1.1%
Linux;MacOS	23	1.0%
AWS	19	0.8%
Microsoft Azure;Windows	16	0.7%
Other values (938)	1682	74.4%
(Missing)	196	8.7%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
cloud	818	15.3%
pi	322	6.0%
aws;docker;google	203	3.8%
apps	168	3.1%
and	168	3.1%
pi;windows	147	2.7%
azure	145	2.7%
azure;windows	136	2.5%
azure;raspberry	131	2.4%
or	119	2.2%
Other values (559)	2993	55.9%

Most occurring characters

Value	Count	Frequency (%)
o	6944	8.7%
;	6425	8.1%
r	5923	7.4%
e	5484	6.9%
i	4114	5.2%
n	4057	5.1%
u	3885	4.9%
	3285	4.1%
s	3054	3.8%
d	2731	3.4%
Other values (29)	33744	42.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	55064	69.1%
Uppercase Letter	14872	18.7%
Other Punctuation	6425	8.1%
Space Separator	3285	4.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	6944	12.6%
r	5923	10.8%
e	5484	10.0%
i	4114	7.5%
n	4057	7.4%
u	3885	7.1%
s	3054	5.5%
d	2731	5.0%
a	2389	4.3%
l	2384	4.3%
Other values (12)	14099	25.6%

Uppercase Letter

Value	Count	Frequency (%)
A	2187	14.7%
S	1796	12.1%
W	1783	12.0%
L	1420	9.5%
P	1367	9.2%
D	1140	7.7%
M	1078	7.2%
C	818	5.5%
K	725	4.9%
O	711	4.8%
Other values (5)	1847	12.4%

Other Punctuation

Value	Count	Frequency (%)
;	6425	100.0%

Space Separator

Value	Count	Frequency (%)
	3285	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	69936	87.8%
Common	9710	12.2%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	6944	9.9%
r	5923	8.5%
e	5484	7.8%
i	4114	5.9%
n	4057	5.8%
u	3885	5.6%
s	3054	4.4%
d	2731	3.9%
a	2389	3.4%
l	2384	3.4%
Other values (27)	28971	41.4%

Common

Value	Count	Frequency (%)
;	6425	66.2%
	3285	33.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	79646	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	6944	8.7%
;	6425	8.1%
r	5923	7.4%
e	5484	6.9%
i	4114	5.2%
n	4057	5.1%
u	3885	4.9%
	3285	4.1%
s	3054	3.8%
d	2731	3.4%
Other values (29)	33744	42.4%

platform_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct	897
Distinct (%)	41.3%
Missing	91
Missing (%)	4.0%
Memory size	17.8 KiB

Windows	125
Linux;Windows	116
Linux	100
Docker;Linux	75
Docker;Linux;Windows	45
Other values (892)	1709

Length

Max length	166
Median length	116
Mean length	34.12626728
Min length	3

Characters and Unicode

Total characters	74054
Distinct characters	39
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	656 ?
Unique (%)	30.2%

Sample

1st row	MacOS;Windows
2nd row	Arduino;AWS;Linux;Microsoft Azure;Raspberry Pi
3rd row	Linux;Microsoft Azure
4th row	Docker
5th row	Docker;Kubernetes;Linux;Raspberry Pi;Windows

Common Values

Value	Count	Frequency (%)
Windows	125	5.5%
Linux;Windows	116	5.1%
Linux	100	4.4%
Docker;Linux	75	3.3%
Docker;Linux;Windows	45	2.0%
Microsoft Azure;Windows	28	1.2%
AWS;Docker;Linux	25	1.1%
Linux;MacOS	23	1.0%
AWS;Linux	19	0.8%
Docker;Linux;MacOS	19	0.8%
Other values (887)	1595	70.5%
(Missing)	91	4.0%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
cloud	598	12.4%
apps	184	3.8%
and	184	3.8%
azure;windows	174	3.6%
pi;windows	153	3.2%
pi	139	2.9%
aws;docker;google	131	2.7%
windows	125	2.6%
linux;windows	116	2.4%
linux	100	2.1%
Other values (574)	2901	60.4%

Most occurring characters

Value	Count	Frequency (%)
o	6562	8.9%
;	6217	8.4%
r	5099	6.9%
i	4506	6.1%
n	4333	5.9%
e	4095	5.5%
u	3395	4.6%
s	3226	4.4%
d	3143	4.2%
	2635	3.6%
Other values (29)	30843	41.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	51028	68.9%
Uppercase Letter	14174	19.1%
Other Punctuation	6217	8.4%
Space Separator	2635	3.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
o	6562	12.9%
r	5099	10.0%
i	4506	8.8%
n	4333	8.5%
e	4095	8.0%
u	3395	6.7%
s	3226	6.3%
d	3143	6.2%
c	2252	4.4%
a	2158	4.2%
Other values (12)	12259	24.0%

Uppercase Letter

Value	Count	Frequency (%)
W	2280	16.1%
A	2031	14.3%
S	1781	12.6%
L	1591	11.2%
P	1160	8.2%
M	1087	7.7%
D	1055	7.4%
O	768	5.4%
C	598	4.2%
G	524	3.7%
Other values (5)	1299	9.2%

Other Punctuation

Value	Count	Frequency (%)
;	6217	100.0%

Space Separator

Value	Count	Frequency (%)
	2635	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	65202	88.0%
Common	8852	12.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
o	6562	10.1%
r	5099	7.8%
i	4506	6.9%
n	4333	6.6%
e	4095	6.3%
u	3395	5.2%
s	3226	4.9%
d	3143	4.8%
W	2280	3.5%
c	2252	3.5%
Other values (27)	26311	40.4%

Common

Value	Count	Frequency (%)
;	6217	70.2%
	2635	29.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	74054	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
o	6562	8.9%
;	6217	8.4%
r	5099	6.9%
i	4506	6.1%
n	4333	5.9%
e	4095	5.5%
u	3395	4.6%
s	3226	4.4%
d	3143	4.2%
	2635	3.6%
Other values (29)	30843	41.6%

purchase_what
Categorical

MISSING

Distinct	3
Distinct (%)	0.1%
Missing	208
Missing (%)	9.2%
Memory size	17.8 KiB

I have some influence	873
I have little or no influence	733
I have a great deal of influence	447

Length

Max length	32
Median length	29
Mean length	26.2513395
Min length	21

Characters and Unicode

Total characters	53894
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	I have little or no influence
2nd row	I have some influence
3rd row	I have some influence
4th row	I have little or no influence
5th row	I have some influence

Common Values

Value	Count	Frequency (%)
I have some influence	873	38.6%
I have little or no influence	733	32.4%
I have a great deal of influence	447	19.8%
(Missing)	208	9.2%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
i	2053	18.6%
have	2053	18.6%
influence	2053	18.6%
some	873	7.9%
little	733	6.7%
or	733	6.7%
no	733	6.7%
a	447	4.1%
great	447	4.1%
deal	447	4.1%

Most occurring characters

Value	Count	Frequency (%)
	8966	16.6%
e	8659	16.1%
n	4839	9.0%
l	3966	7.4%
a	3394	6.3%
i	2786	5.2%
o	2786	5.2%
f	2500	4.6%
c	2053	3.8%
u	2053	3.8%
Other values (9)	11892	22.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	42875	79.6%
Space Separator	8966	16.6%
Uppercase Letter	2053	3.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	8659	20.2%
n	4839	11.3%
l	3966	9.3%
a	3394	7.9%
i	2786	6.5%
o	2786	6.5%
f	2500	5.8%
c	2053	4.8%
u	2053	4.8%
v	2053	4.8%
Other values (7)	7786	18.2%

Space Separator

Value	Count	Frequency (%)
	8966	100.0%

Uppercase Letter

Value	Count	Frequency (%)
I	2053	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	44928	83.4%
Common	8966	16.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	8659	19.3%
n	4839	10.8%
l	3966	8.8%
a	3394	7.6%
i	2786	6.2%
o	2786	6.2%
f	2500	5.6%
c	2053	4.6%
u	2053	4.6%
I	2053	4.6%
Other values (8)	9839	21.9%

Common

Value	Count	Frequency (%)
	8966	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	53894	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	8966	16.6%
e	8659	16.1%
n	4839	9.0%
l	3966	7.4%
a	3394	6.3%
i	2786	5.2%
o	2786	5.2%
f	2500	4.6%
c	2053	3.8%
u	2053	3.8%
Other values (9)	11892	22.1%

sexuality
Categorical

HIGH CORRELATION
MISSING

Distinct	12
Distinct (%)	0.6%
Missing	207
Missing (%)	9.2%
Memory size	17.8 KiB

Straight / Heterosexual	1862
Bisexual	96
Gay or Lesbian	45
Bisexual;Straight / Heterosexual	14
Bisexual;Queer	11
Other values (7)	26

Length

Max length	53
Median length	23
Mean length	22.09737098
Min length	5

Characters and Unicode

Total characters	45388
Distinct characters	24
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	3 ?
Unique (%)	0.1%

Sample

1st row	Straight / Heterosexual
2nd row	Straight / Heterosexual
3rd row	Bisexual
4th row	Straight / Heterosexual
5th row	Straight / Heterosexual

Common Values

Value	Count	Frequency (%)
Straight / Heterosexual	1862	82.4%
Bisexual	96	4.2%
Gay or Lesbian	45	2.0%
Bisexual;Straight / Heterosexual	14	0.6%
Bisexual;Queer	11	0.5%
Queer	10	0.4%
Straight / Heterosexual;Queer	6	0.3%
Gay or Lesbian;Queer	4	0.2%
Bisexual;Gay or Lesbian;Straight / Heterosexual;Queer	3	0.1%
Bisexual;Gay or Lesbian;Straight / Heterosexual	1	< 0.1%
Other values (2)	2	0.1%
(Missing)	207	9.2%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
	1886	31.8%
heterosexual	1877	31.6%
straight	1868	31.5%
bisexual	96	1.6%
or	55	0.9%
gay	49	0.8%
lesbian	46	0.8%
bisexual;straight	14	0.2%
bisexual;queer	11	0.2%
queer	10	0.2%
Other values (4)	24	0.4%

Most occurring characters

Value	Count	Frequency (%)
e	5910	13.0%
t	5658	12.5%
a	4009	8.8%
	3882	8.6%
r	3862	8.5%
s	2068	4.6%
i	2068	4.6%
u	2048	4.5%
l	2013	4.4%
x	2013	4.4%
Other values (14)	11857	26.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	35527	78.3%
Uppercase Letter	4044	8.9%
Space Separator	3882	8.6%
Other Punctuation	1935	4.3%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	5910	16.6%
t	5658	15.9%
a	4009	11.3%
r	3862	10.9%
s	2068	5.8%
i	2068	5.8%
u	2048	5.8%
l	2013	5.7%
x	2013	5.7%
o	1941	5.5%
Other values (5)	3937	11.1%

Uppercase Letter

Value	Count	Frequency (%)
S	1886	46.6%
H	1886	46.6%
B	127	3.1%
G	55	1.4%
L	55	1.4%
Q	35	0.9%

Other Punctuation

Value	Count	Frequency (%)
/	1886	97.5%
;	49	2.5%

Space Separator

Value	Count	Frequency (%)
	3882	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	39571	87.2%
Common	5817	12.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	5910	14.9%
t	5658	14.3%
a	4009	10.1%
r	3862	9.8%
s	2068	5.2%
i	2068	5.2%
u	2048	5.2%
l	2013	5.1%
x	2013	5.1%
o	1941	4.9%
Other values (11)	7981	20.2%

Common

Value	Count	Frequency (%)
	3882	66.7%
/	1886	32.4%
;	49	0.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	45388	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	5910	13.0%
t	5658	12.5%
a	4009	8.8%
	3882	8.6%
r	3862	8.5%
s	2068	4.6%
i	2068	4.6%
u	2048	4.5%
l	2013	4.4%
x	2013	4.4%
Other values (14)	11857	26.1%

so_account
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	3
Distinct (%)	0.1%
Missing	14
Missing (%)	0.6%
Memory size	17.8 KiB

Yes	1885
No	237
Not sure/can't remember	125

Length

Max length	23
Median length	3
Mean length	4.007120605
Min length	2

Characters and Unicode

Total characters	9004
Distinct characters	16
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes
2nd row	Yes
3rd row	Yes
4th row	Not sure/can't remember
5th row	Yes

Common Values

Value	Count	Frequency (%)
Yes	1885	83.4%
No	237	10.5%
Not sure/can't remember	125	5.5%
(Missing)	14	0.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
yes	1885	75.5%
no	237	9.5%
not	125	5.0%
sure/can't	125	5.0%
remember	125	5.0%

Most occurring characters

Value	Count	Frequency (%)
e	2385	26.5%
s	2010	22.3%
Y	1885	20.9%
r	375	4.2%
N	362	4.0%
o	362	4.0%
t	250	2.8%
	250	2.8%
m	250	2.8%
u	125	1.4%
Other values (6)	750	8.3%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	6257	69.5%
Uppercase Letter	2247	25.0%
Space Separator	250	2.8%
Other Punctuation	250	2.8%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2385	38.1%
s	2010	32.1%
r	375	6.0%
o	362	5.8%
t	250	4.0%
m	250	4.0%
u	125	2.0%
c	125	2.0%
a	125	2.0%
n	125	2.0%

Uppercase Letter

Value	Count	Frequency (%)
Y	1885	83.9%
N	362	16.1%

Other Punctuation

Value	Count	Frequency (%)
/	125	50.0%
'	125	50.0%

Space Separator

Value	Count	Frequency (%)
	250	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	8504	94.4%
Common	500	5.6%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2385	28.0%
s	2010	23.6%
Y	1885	22.2%
r	375	4.4%
N	362	4.3%
o	362	4.3%
t	250	2.9%
m	250	2.9%
u	125	1.5%
c	125	1.5%
Other values (3)	375	4.4%

Common

Value	Count	Frequency (%)
	250	50.0%
/	125	25.0%
'	125	25.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	9004	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	2385	26.5%
s	2010	22.3%
Y	1885	20.9%
r	375	4.2%
N	362	4.0%
o	362	4.0%
t	250	2.8%
	250	2.8%
m	250	2.8%
u	125	1.4%
Other values (6)	750	8.3%

so_comm
Categorical

HIGH CORRELATION

Distinct	6
Distinct (%)	0.3%
Missing	13
Missing (%)	0.6%
Memory size	17.8 KiB

Yes, somewhat	744
No, not really	567
Neutral	399
Yes, definitely	381
No, not at all	140

Length

Max length	15
Median length	14
Mean length	12.55071174
Min length	7

Characters and Unicode

Total characters	28214
Distinct characters	20
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Yes, somewhat
2nd row	Yes, definitely
3rd row	Yes, somewhat
4th row	No, not really
5th row	Yes, somewhat

Common Values

Value	Count	Frequency (%)
Yes, somewhat	744	32.9%
No, not really	567	25.1%
Neutral	399	17.6%
Yes, definitely	381	16.9%
No, not at all	140	6.2%
Not sure	17	0.8%
(Missing)	13	0.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
yes	1125	22.8%
somewhat	744	15.0%
not	724	14.6%
no	707	14.3%
really	567	11.5%
neutral	399	8.1%
definitely	381	7.7%
at	140	2.8%
all	140	2.8%
sure	17	0.3%

Most occurring characters

Value	Count	Frequency (%)
e	3614	12.8%
	2696	9.6%
t	2388	8.5%
l	2194	7.8%
o	2175	7.7%
a	1990	7.1%
s	1886	6.7%
,	1832	6.5%
Y	1125	4.0%
N	1123	4.0%
Other values (10)	7191	25.5%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	21438	76.0%
Space Separator	2696	9.6%
Uppercase Letter	2248	8.0%
Other Punctuation	1832	6.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	3614	16.9%
t	2388	11.1%
l	2194	10.2%
o	2175	10.1%
a	1990	9.3%
s	1886	8.8%
n	1088	5.1%
r	983	4.6%
y	948	4.4%
i	762	3.6%
Other values (6)	3410	15.9%

Uppercase Letter

Value	Count	Frequency (%)
Y	1125	50.0%
N	1123	50.0%

Space Separator

Value	Count	Frequency (%)
	2696	100.0%

Other Punctuation

Value	Count	Frequency (%)
,	1832	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	23686	84.0%
Common	4528	16.0%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	3614	15.3%
t	2388	10.1%
l	2194	9.3%
o	2175	9.2%
a	1990	8.4%
s	1886	8.0%
Y	1125	4.7%
N	1123	4.7%
n	1088	4.6%
r	983	4.2%
Other values (8)	5120	21.6%

Common

Value	Count	Frequency (%)
	2696	59.5%
,	1832	40.5%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	28214	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	3614	12.8%
	2696	9.6%
t	2388	8.5%
l	2194	7.8%
o	2175	7.7%
a	1990	7.1%
s	1886	6.7%
,	1832	6.5%
Y	1125	4.0%
N	1123	4.0%
Other values (10)	7191	25.5%

so_part_freq
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	6
Distinct (%)	0.3%
Missing	377
Missing (%)	16.7%
Memory size	17.8 KiB

Less than once per month or monthly	785
A few times per month or weekly	401
I have never participated in Q&A on Stack Overflow	309
A few times per week	212
Daily or almost daily	113

Length

Max length	50
Median length	35
Mean length	33.6395966
Min length	20

Characters and Unicode

Total characters	63377
Distinct characters	30
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Less than once per month or monthly
2nd row	A few times per week
3rd row	A few times per month or weekly
4th row	A few times per month or weekly
5th row	Less than once per month or monthly

Common Values

Value	Count	Frequency (%)
Less than once per month or monthly	785	34.7%
A few times per month or weekly	401	17.7%
I have never participated in Q&A on Stack Overflow	309	13.7%
A few times per week	212	9.4%
Daily or almost daily	113	5.0%
Multiple times per day	64	2.8%
(Missing)	377	16.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
per	1462	11.4%
or	1299	10.1%
month	1186	9.2%
less	785	6.1%
once	785	6.1%
monthly	785	6.1%
than	785	6.1%
times	677	5.3%
a	613	4.8%
few	613	4.8%
Other values (15)	3861	30.0%

Most occurring characters

Value	Count	Frequency (%)
	10967	17.3%
e	7157	11.3%
o	4786	7.6%
t	4537	7.2%
n	4468	7.0%
r	3688	5.8%
h	3065	4.8%
m	2761	4.4%
a	2424	3.8%
s	2360	3.7%
Other values (20)	17164	27.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	48981	77.3%
Space Separator	10967	17.3%
Uppercase Letter	3120	4.9%
Other Punctuation	309	0.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	7157	14.6%
o	4786	9.8%
t	4537	9.3%
n	4468	9.1%
r	3688	7.5%
h	3065	6.3%
m	2761	5.6%
a	2424	4.9%
s	2360	4.8%
p	2144	4.4%
Other values (10)	11591	23.7%

Uppercase Letter

Value	Count	Frequency (%)
A	922	29.6%
L	785	25.2%
I	309	9.9%
Q	309	9.9%
S	309	9.9%
O	309	9.9%
D	113	3.6%
M	64	2.1%

Space Separator

Value	Count	Frequency (%)
	10967	100.0%

Other Punctuation

Value	Count	Frequency (%)
&	309	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	52101	82.2%
Common	11276	17.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	7157	13.7%
o	4786	9.2%
t	4537	8.7%
n	4468	8.6%
r	3688	7.1%
h	3065	5.9%
m	2761	5.3%
a	2424	4.7%
s	2360	4.5%
p	2144	4.1%
Other values (18)	14711	28.2%

Common

Value	Count	Frequency (%)
	10967	97.3%
&	309	2.7%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	63377	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	10967	17.3%
e	7157	11.3%
o	4786	7.6%
t	4537	7.2%
n	4468	7.0%
r	3688	5.8%
h	3065	4.8%
m	2761	4.4%
a	2424	3.8%
s	2360	3.7%
Other values (20)	17164	27.1%

so_visit_freq
Categorical

Distinct	6
Distinct (%)	0.3%
Missing	13
Missing (%)	0.6%
Memory size	17.8 KiB

Multiple times per day	794
Daily or almost daily	734
A few times per week	479
A few times per month or weekly	202
Less than once per month or monthly	38

Length

Max length	50
Median length	35
Mean length	22.28825623
Min length	20

Characters and Unicode

Total characters	50104
Distinct characters	31
Distinct categories	5 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	1 ?
Unique (%)	< 0.1%

Sample

1st row	Multiple times per day
2nd row	A few times per week
3rd row	A few times per week
4th row	Multiple times per day
5th row	Daily or almost daily

Common Values

Value	Count	Frequency (%)
Multiple times per day	794	35.1%
Daily or almost daily	734	32.5%
A few times per week	479	21.2%
A few times per month or weekly	202	8.9%
Less than once per month or monthly	38	1.7%
I have never visited Stack Overflow (before today)	1	< 0.1%
(Missing)	13	0.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
per	1513	14.8%
times	1475	14.5%
daily	1468	14.4%
or	974	9.6%
multiple	794	7.8%
day	794	7.8%
almost	734	7.2%
a	681	6.7%
few	681	6.7%
week	479	4.7%
Other values (14)	602	5.9%

Most occurring characters

Value	Count	Frequency (%)
	7947	15.9%
e	5908	11.8%
l	4031	8.0%
i	3739	7.5%
t	3322	6.6%
a	3037	6.1%
y	2503	5.0%
r	2490	5.0%
m	2487	5.0%
p	2307	4.6%
Other values (21)	12333	24.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	39905	79.6%
Space Separator	7947	15.9%
Uppercase Letter	2250	4.5%
Open Punctuation	1	< 0.1%
Close Punctuation	1	< 0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	5908	14.8%
l	4031	10.1%
i	3739	9.4%
t	3322	8.3%
a	3037	7.6%
y	2503	6.3%
r	2490	6.2%
m	2487	6.2%
p	2307	5.8%
s	2286	5.7%
Other values (11)	7795	19.5%

Uppercase Letter

Value	Count	Frequency (%)
M	794	35.3%
D	734	32.6%
A	681	30.3%
L	38	1.7%
I	1	< 0.1%
S	1	< 0.1%
O	1	< 0.1%

Space Separator

Value	Count	Frequency (%)
	7947	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	1	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	1	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	42155	84.1%
Common	7949	15.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	5908	14.0%
l	4031	9.6%
i	3739	8.9%
t	3322	7.9%
a	3037	7.2%
y	2503	5.9%
r	2490	5.9%
m	2487	5.9%
p	2307	5.5%
s	2286	5.4%
Other values (18)	10045	23.8%

Common

Value	Count	Frequency (%)
	7947	> 99.9%
(	1	< 0.1%
)	1	< 0.1%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	50104	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	7947	15.9%
e	5908	11.8%
l	4031	8.0%
i	3739	7.5%
t	3322	6.6%
a	3037	6.1%
y	2503	5.0%
r	2490	5.0%
m	2487	5.0%
p	2307	4.6%
Other values (21)	12333	24.6%

survey_ease
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	13
Missing (%)	0.6%
Memory size	17.8 KiB

Easy	1569
Neither easy nor difficult	660
Difficult	19

Length

Max length	26
Median length	4
Mean length	10.50133452
Min length	4

Characters and Unicode

Total characters	23607
Distinct characters	19
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Easy
2nd row	Neither easy nor difficult
3rd row	Easy
4th row	Easy
5th row	Easy

Common Values

Value	Count	Frequency (%)
Easy	1569	69.4%
Neither easy nor difficult	660	29.2%
Difficult	19	0.8%
(Missing)	13	0.6%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
easy	2229	52.7%
difficult	679	16.1%
neither	660	15.6%
nor	660	15.6%

Most occurring characters

Value	Count	Frequency (%)
a	2229	9.4%
s	2229	9.4%
y	2229	9.4%
i	2018	8.5%
e	1980	8.4%
	1980	8.4%
E	1569	6.6%
f	1358	5.8%
t	1339	5.7%
r	1320	5.6%
Other values (9)	5356	22.7%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	19379	82.1%
Uppercase Letter	2248	9.5%
Space Separator	1980	8.4%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	2229	11.5%
s	2229	11.5%
y	2229	11.5%
i	2018	10.4%
e	1980	10.2%
f	1358	7.0%
t	1339	6.9%
r	1320	6.8%
c	679	3.5%
u	679	3.5%
Other values (5)	3319	17.1%

Uppercase Letter

Value	Count	Frequency (%)
E	1569	69.8%
N	660	29.4%
D	19	0.8%

Space Separator

Value	Count	Frequency (%)
	1980	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	21627	91.6%
Common	1980	8.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	2229	10.3%
s	2229	10.3%
y	2229	10.3%
i	2018	9.3%
e	1980	9.2%
E	1569	7.3%
f	1358	6.3%
t	1339	6.2%
r	1320	6.1%
c	679	3.1%
Other values (8)	4677	21.6%

Common

Value	Count	Frequency (%)
	1980	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	23607	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	2229	9.4%
s	2229	9.4%
y	2229	9.4%
i	2018	8.5%
e	1980	8.4%
	1980	8.4%
E	1569	6.6%
f	1358	5.8%
t	1339	5.7%
r	1320	5.6%
Other values (9)	5356	22.7%

survey_length
Categorical

Distinct	3
Distinct (%)	0.1%
Missing	10
Missing (%)	0.4%
Memory size	17.8 KiB

Appropriate in length	1756
Too long	392
Too short	103

Length

Max length	21
Median length	21
Mean length	18.18702799
Min length	8

Characters and Unicode

Total characters	40939
Distinct characters	15
Distinct categories	3 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Appropriate in length
2nd row	Appropriate in length
3rd row	Too short
4th row	Appropriate in length
5th row	Appropriate in length

Common Values

Value	Count	Frequency (%)
Appropriate in length	1756	77.7%
Too long	392	17.3%
Too short	103	4.6%
(Missing)	10	0.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
appropriate	1756	28.1%
in	1756	28.1%
length	1756	28.1%
too	495	7.9%
long	392	6.3%
short	103	1.6%

Most occurring characters

Value	Count	Frequency (%)
p	5268	12.9%
	4007	9.8%
n	3904	9.5%
r	3615	8.8%
t	3615	8.8%
i	3512	8.6%
e	3512	8.6%
o	3241	7.9%
l	2148	5.2%
g	2148	5.2%
Other values (5)	5969	14.6%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	34681	84.7%
Space Separator	4007	9.8%
Uppercase Letter	2251	5.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
p	5268	15.2%
n	3904	11.3%
r	3615	10.4%
t	3615	10.4%
i	3512	10.1%
e	3512	10.1%
o	3241	9.3%
l	2148	6.2%
g	2148	6.2%
h	1859	5.4%
Other values (2)	1859	5.4%

Uppercase Letter

Value	Count	Frequency (%)
A	1756	78.0%
T	495	22.0%

Space Separator

Value	Count	Frequency (%)
	4007	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	36932	90.2%
Common	4007	9.8%

Most frequent character per script

Latin

Value	Count	Frequency (%)
p	5268	14.3%
n	3904	10.6%
r	3615	9.8%
t	3615	9.8%
i	3512	9.5%
e	3512	9.5%
o	3241	8.8%
l	2148	5.8%
g	2148	5.8%
h	1859	5.0%
Other values (4)	4110	11.1%

Common

Value	Count	Frequency (%)
	4007	100.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	40939	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
p	5268	12.9%
	4007	9.8%
n	3904	9.5%
r	3615	8.8%
t	3615	8.8%
i	3512	8.6%
e	3512	8.6%
o	3241	7.9%
l	2148	5.2%
g	2148	5.2%
Other values (5)	5969	14.6%

trans
Boolean

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct	2
Distinct (%)	0.1%
Missing	62
Missing (%)	2.7%
Memory size	4.5 KiB

False	2180
True	19
(Missing)	62

Common Values
Category Frequency Plot

Value	Count	Frequency (%)
False	2180	96.4%
True	19	0.8%
(Missing)	62	2.7%

undergrad_major
Categorical

MISSING

Distinct	12
Distinct (%)	0.6%
Missing	110
Missing (%)	4.9%
Memory size	17.8 KiB

Computer science, computer engineering, or software engineering	1052
Mathematics or statistics	286
A natural science (such as biology, chemistry, physics, etc.)	264
Another engineering discipline (such as civil, electrical, mechanical, etc.)	252
Information systems, information technology, or system administration	80
Other values (7)	217

Length

Max length	78
Median length	76
Mean length	59.967457
Min length	24

Characters and Unicode

Total characters	128990
Distinct characters	34
Distinct categories	6 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Computer science, computer engineering, or software engineering
2nd row	A natural science (such as biology, chemistry, physics, etc.)
3rd row	Computer science, computer engineering, or software engineering
4th row	Another engineering discipline (such as civil, electrical, mechanical, etc.)
5th row	Mathematics or statistics

Common Values

Value	Count	Frequency (%)
Computer science, computer engineering, or software engineering	1052	46.5%
Mathematics or statistics	286	12.6%
A natural science (such as biology, chemistry, physics, etc.)	264	11.7%
Another engineering discipline (such as civil, electrical, mechanical, etc.)	252	11.1%
Information systems, information technology, or system administration	80	3.5%
A social science (such as anthropology, psychology, political science, etc.)	74	3.3%
A business discipline (such as accounting, finance, marketing, etc.)	64	2.8%
A humanities discipline (such as literature, history, philosophy, etc.)	36	1.6%
A health science (such as nursing, pharmacy, radiology, etc.)	17	0.8%
Web development or web design	11	0.5%
Other values (2)	15	0.7%
(Missing)	110	4.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
engineering	2356	15.3%
computer	2104	13.6%
science	1481	9.6%
or	1439	9.3%
software	1052	6.8%
such	717	4.6%
as	717	4.6%
etc	717	4.6%
a	460	3.0%
discipline	352	2.3%
Other values (45)	4034	26.1%

Most occurring characters

Value	Count	Frequency (%)
e	16486	12.8%
	13278	10.3%
i	11150	8.6%
n	10814	8.4%
c	8696	6.7%
r	8595	6.7%
t	7383	5.7%
s	7261	5.6%
o	6695	5.2%
g	5407	4.2%
Other values (24)	33225	25.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	106995	82.9%
Space Separator	13278	10.3%
Other Punctuation	5132	4.0%
Uppercase Letter	2151	1.7%
Open Punctuation	717	0.6%
Close Punctuation	717	0.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	16486	15.4%
i	11150	10.4%
n	10814	10.1%
c	8696	8.1%
r	8595	8.0%
t	7383	6.9%
s	7261	6.8%
o	6695	6.3%
g	5407	5.1%
a	4840	4.5%
Other values (13)	19668	18.4%

Uppercase Letter

Value	Count	Frequency (%)
C	1052	48.9%
A	707	32.9%
M	286	13.3%
I	85	4.0%
W	11	0.5%
F	10	0.5%

Other Punctuation

Value	Count	Frequency (%)
,	4415	86.0%
.	717	14.0%

Space Separator

Value	Count	Frequency (%)
	13278	100.0%

Open Punctuation

Value	Count	Frequency (%)
(	717	100.0%

Close Punctuation

Value	Count	Frequency (%)
)	717	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	109146	84.6%
Common	19844	15.4%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	16486	15.1%
i	11150	10.2%
n	10814	9.9%
c	8696	8.0%
r	8595	7.9%
t	7383	6.8%
s	7261	6.7%
o	6695	6.1%
g	5407	5.0%
a	4840	4.4%
Other values (19)	21819	20.0%

Common

Value	Count	Frequency (%)
	13278	66.9%
,	4415	22.2%
(	717	3.6%
.	717	3.6%
)	717	3.6%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	128990	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
e	16486	12.8%
	13278	10.3%
i	11150	8.6%
n	10814	8.4%
c	8696	6.7%
r	8595	6.7%
t	7383	5.7%
s	7261	5.6%
o	6695	5.2%
g	5407	4.2%
Other values (24)	33225	25.8%

webframe_desire_next_year
Categorical

HIGH CARDINALITY
MISSING

Distinct	435
Distinct (%)	32.6%
Missing	925
Missing (%)	40.9%
Memory size	17.8 KiB

Flask	145
Django;Flask	89
React.js	82
Django	64
Vue.js	31
Other values (430)	925

Length

Max length	134
Median length	91
Mean length	20.0755988
Min length	5

Characters and Unicode

Total characters	26821
Distinct characters	38
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	304 ?
Unique (%)	22.8%

Sample

1st row	Express;React.js
2nd row	Flask;Spring
3rd row	Django;Flask
4th row	ASP.NET Core
5th row	Django;Express;Flask;jQuery;Laravel;React.js

Common Values

Value	Count	Frequency (%)
Flask	145	6.4%
Django;Flask	89	3.9%
React.js	82	3.6%
Django	64	2.8%
Vue.js	31	1.4%
ASP.NET Core	27	1.2%
Flask;React.js	26	1.1%
Django;Flask;React.js	22	1.0%
Spring	21	0.9%
jQuery	19	0.8%
Other values (425)	810	35.8%
(Missing)	925	40.9%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
flask	145	8.4%
on	92	5.3%
asp.net	91	5.3%
django;flask	89	5.2%
react.js	82	4.8%
django	64	3.7%
core	52	3.0%
asp.net;asp.net	45	2.6%
rails	44	2.6%
vue.js	31	1.8%
Other values (395)	990	57.4%

Most occurring characters

Value	Count	Frequency (%)
a	2344	8.7%
;	2167	8.1%
s	2150	8.0%
j	1732	6.5%
e	1583	5.9%
.	1335	5.0%
r	1268	4.7%
l	1190	4.4%
n	1144	4.3%
u	1078	4.0%
Other values (28)	10830	40.4%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	17585	65.6%
Uppercase Letter	5345	19.9%
Other Punctuation	3502	13.1%
Space Separator	389	1.5%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	2344	13.3%
s	2150	12.2%
j	1732	9.8%
e	1583	9.0%
r	1268	7.2%
l	1190	6.8%
n	1144	6.5%
u	1078	6.1%
g	1013	5.8%
o	808	4.6%
Other values (11)	3275	18.6%

Uppercase Letter

Value	Count	Frequency (%)
R	727	13.6%
A	712	13.3%
F	596	11.2%
E	498	9.3%
D	490	9.2%
S	486	9.1%
V	331	6.2%
N	309	5.8%
P	309	5.8%
T	309	5.8%
Other values (4)	578	10.8%

Other Punctuation

Value	Count	Frequency (%)
;	2167	61.9%
.	1335	38.1%

Space Separator

Value	Count	Frequency (%)
	389	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	22930	85.5%
Common	3891	14.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	2344	10.2%
s	2150	9.4%
j	1732	7.6%
e	1583	6.9%
r	1268	5.5%
l	1190	5.2%
n	1144	5.0%
u	1078	4.7%
g	1013	4.4%
o	808	3.5%
Other values (25)	8620	37.6%

Common

Value	Count	Frequency (%)
;	2167	55.7%
.	1335	34.3%
	389	10.0%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	26821	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
a	2344	8.7%
;	2167	8.1%
s	2150	8.0%
j	1732	6.5%
e	1583	5.9%
.	1335	5.0%
r	1268	4.7%
l	1190	4.4%
n	1144	4.3%
u	1078	4.0%
Other values (28)	10830	40.4%

webframe_worked_with
Categorical

HIGH CARDINALITY
MISSING

Distinct	502
Distinct (%)	34.6%
Missing	809
Missing (%)	35.8%
Memory size	17.8 KiB

Flask	195
Django	74
Django;Flask	57
React.js	53
jQuery	45
Other values (497)	1028

Length

Max length	134
Median length	89
Mean length	20.53787879
Min length	5

Characters and Unicode

Total characters	29821
Distinct characters	38
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	350 ?
Unique (%)	24.1%

Sample

1st row	Express;React.js
2nd row	Flask;Spring
3rd row	Django;Flask
4th row	Express;Flask
5th row	ASP.NET;ASP.NET Core;jQuery;Vue.js

Common Values

Value	Count	Frequency (%)
Flask	195	8.6%
Django	74	3.3%
Django;Flask	57	2.5%
React.js	53	2.3%
jQuery	45	2.0%
Flask;jQuery	29	1.3%
Spring	24	1.1%
Django;Flask;React.js	23	1.0%
Flask;React.js	22	1.0%
Django;jQuery	21	0.9%
Other values (492)	909	40.2%
(Missing)	809	35.8%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
flask	195	10.7%
on	83	4.6%
django	74	4.1%
asp.net	65	3.6%
asp.net;asp.net	64	3.5%
django;flask	57	3.1%
rails	54	3.0%
react.js	53	2.9%
jquery	45	2.5%
angular;angular.js;asp.net;asp.net	41	2.3%
Other values (466)	1083	59.7%

Most occurring characters

Value	Count	Frequency (%)
;	2487	8.3%
a	2405	8.1%
s	2124	7.1%
j	1812	6.1%
r	1792	6.0%
e	1724	5.8%
l	1380	4.6%
u	1336	4.5%
.	1285	4.3%
n	1228	4.1%
Other values (28)	12248	41.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	19344	64.9%
Uppercase Letter	6343	21.3%
Other Punctuation	3772	12.6%
Space Separator	362	1.2%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
a	2405	12.4%
s	2124	11.0%
j	1812	9.4%
r	1792	9.3%
e	1724	8.9%
l	1380	7.1%
u	1336	6.9%
n	1228	6.3%
g	1097	5.7%
y	754	3.9%
Other values (11)	3692	19.1%

Uppercase Letter

Value	Count	Frequency (%)
A	897	14.1%
S	676	10.7%
F	668	10.5%
E	659	10.4%
R	612	9.6%
Q	530	8.4%
D	467	7.4%
N	425	6.7%
T	425	6.7%
P	425	6.7%
Other values (4)	559	8.8%

Other Punctuation

Value	Count	Frequency (%)
;	2487	65.9%
.	1285	34.1%

Space Separator

Value	Count	Frequency (%)
	362	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	25687	86.1%
Common	4134	13.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
a	2405	9.4%
s	2124	8.3%
j	1812	7.1%
r	1792	7.0%
e	1724	6.7%
l	1380	5.4%
u	1336	5.2%
n	1228	4.8%
g	1097	4.3%
A	897	3.5%
Other values (25)	9892	38.5%

Common

Value	Count	Frequency (%)
;	2487	60.2%
.	1285	31.1%
	362	8.8%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	29821	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
;	2487	8.3%
a	2405	8.1%
s	2124	7.1%
j	1812	6.1%
r	1792	6.0%
e	1724	5.8%
l	1380	4.6%
u	1336	4.5%
.	1285	4.3%
n	1228	4.1%
Other values (28)	12248	41.1%

welcome_change
Categorical

MISSING

Distinct	6
Distinct (%)	0.3%
Missing	62
Missing (%)	2.7%
Memory size	17.8 KiB

Just as welcome now as I felt last year	1605
Somewhat more welcome now than last year	215
Somewhat less welcome now than last year	144
A lot more welcome now than last year	103
A lot less welcome now than last year	86

Length

Max length	55
Median length	39
Mean length	39.3260573
Min length	37

Characters and Unicode

Total characters	86478
Distinct characters	29
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Just as welcome now as I felt last year
2nd row	Just as welcome now as I felt last year
3rd row	Just as welcome now as I felt last year
4th row	Just as welcome now as I felt last year
5th row	Just as welcome now as I felt last year

Common Values

Value	Count	Frequency (%)
Just as welcome now as I felt last year	1605	71.0%
Somewhat more welcome now than last year	215	9.5%
Somewhat less welcome now than last year	144	6.4%
A lot more welcome now than last year	103	4.6%
A lot less welcome now than last year	86	3.8%
Not applicable - I did not use Stack Overflow last year	46	2.0%
(Missing)	62	2.7%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
as	3210	16.9%
last	2199	11.6%
year	2199	11.6%
welcome	2153	11.3%
now	2153	11.3%
i	1651	8.7%
just	1605	8.5%
felt	1605	8.5%
than	548	2.9%
somewhat	359	1.9%
Other values (11)	1294	6.8%

Most occurring characters

Value	Count	Frequency (%)
	16777	19.4%
e	9155	10.6%
a	8653	10.0%
s	7520	8.7%
t	6643	7.7%
l	6514	7.5%
o	5310	6.1%
w	4711	5.4%
m	2830	3.3%
n	2747	3.2%
Other values (19)	15618	18.1%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	65713	76.0%
Space Separator	16777	19.4%
Uppercase Letter	3942	4.6%
Dash Punctuation	46	0.1%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	9155	13.9%
a	8653	13.2%
s	7520	11.4%
t	6643	10.1%
l	6514	9.9%
o	5310	8.1%
w	4711	7.2%
m	2830	4.3%
n	2747	4.2%
r	2563	3.9%
Other values (11)	9067	13.8%

Uppercase Letter

Value	Count	Frequency (%)
I	1651	41.9%
J	1605	40.7%
S	405	10.3%
A	189	4.8%
N	46	1.2%
O	46	1.2%

Space Separator

Value	Count	Frequency (%)
	16777	100.0%

Dash Punctuation

Value	Count	Frequency (%)
-	46	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	69655	80.5%
Common	16823	19.5%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	9155	13.1%
a	8653	12.4%
s	7520	10.8%
t	6643	9.5%
l	6514	9.4%
o	5310	7.6%
w	4711	6.8%
m	2830	4.1%
n	2747	3.9%
r	2563	3.7%
Other values (17)	13009	18.7%

Common

Value	Count	Frequency (%)
	16777	99.7%
-	46	0.3%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	86478	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	16777	19.4%
e	9155	10.6%
a	8653	10.0%
s	7520	8.7%
t	6643	7.7%
l	6514	7.5%
o	5310	6.1%
w	4711	5.4%
m	2830	3.3%
n	2747	3.2%
Other values (19)	15618	18.1%

work_week_hrs
Real number (ℝ_≥0)

MISSING

Distinct	68
Distinct (%)	3.1%
Missing	42
Missing (%)	1.9%
Infinite	0
Infinite (%)	0.0%
Mean	41.58893646

Minimum	4
Maximum	375
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	4
5-th percentile	20
Q1	40
median	40
Q3	45
95-th percentile	60
Maximum	375
Range	371
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	14.58920078
Coefficient of variation (CV)	0.3507952361
Kurtosis	137.4217825
Mean	41.58893646
Median Absolute Deviation (MAD)	3
Skewness	7.182683506
Sum	92285.85
Variance	212.8447795
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
40	899	39.8%
45	241	10.7%
50	211	9.3%
35	115	5.1%
60	75	3.3%
38	71	3.1%
30	70	3.1%
42	55	2.4%
37	43	1.9%
48	34	1.5%
Other values (58)	405	17.9%
(Missing)	42	1.9%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
4	3	0.1%
5	4	0.2%
6	3	0.1%
7	4	0.2%
8	34	1.5%
9	13	0.6%
10	10	0.4%
11	1	< 0.1%
12	2	0.1%
15	4	0.2%

Value	Count	Frequency (%)
375	1	< 0.1%
168	2	0.1%
160	4	0.2%
150	1	< 0.1%
120	1	< 0.1%
100	3	0.1%
98	1	< 0.1%
90	3	0.1%
88	1	< 0.1%
84	1	< 0.1%

years_code
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	48
Distinct (%)	2.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	14.18487395

Minimum	1
Maximum	50
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	1
5-th percentile	4
Q1	7
median	12
Q3	19
95-th percentile	35
Maximum	50
Range	49
Interquartile range (IQR)	12

Descriptive statistics

Standard deviation	9.177664371
Coefficient of variation (CV)	0.6470035902
Kurtosis	1.269818701
Mean	14.18487395
Median Absolute Deviation (MAD)	5
Skewness	1.241845184
Sum	32072
Variance	84.22952331
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=48)

Value	Count	Frequency (%)
10	213	9.4%
8	144	6.4%
15	143	6.3%
7	142	6.3%
6	135	6.0%
5	129	5.7%
20	117	5.2%
9	111	4.9%
12	108	4.8%
4	88	3.9%
Other values (38)	931	41.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	8	0.4%
2	15	0.7%
3	56	2.5%
4	88	3.9%
5	129	5.7%
6	135	6.0%
7	142	6.3%
8	144	6.4%
9	111	4.9%
10	213	9.4%

Value	Count	Frequency (%)
50	6	0.3%
48	1	< 0.1%
47	1	< 0.1%
45	6	0.3%
44	3	0.1%
43	5	0.2%
42	5	0.2%
41	4	0.2%
40	21	0.9%
39	4	0.2%

years_code_pro
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	42
Distinct (%)	1.9%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	7.758513932

Minimum	1
Maximum	50
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	17.8 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	3
median	5
Q3	10
95-th percentile	23
Maximum	50
Range	49
Interquartile range (IQR)	7

Descriptive statistics

Standard deviation	7.236954959
Coefficient of variation (CV)	0.9327759185
Kurtosis	3.551357021
Mean	7.758513932
Median Absolute Deviation (MAD)	3
Skewness	1.764669474
Sum	17542
Variance	52.37351708
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=42)

Value	Count	Frequency (%)
3	269	11.9%
1	264	11.7%
2	255	11.3%
5	225	10.0%
4	172	7.6%
6	133	5.9%
10	121	5.4%
8	119	5.3%
7	110	4.9%
15	69	3.1%
Other values (32)	524	23.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	264	11.7%
2	255	11.3%
3	269	11.9%
4	172	7.6%
5	225	10.0%
6	133	5.9%
7	110	4.9%
8	119	5.3%
9	62	2.7%
10	121	5.4%

Value	Count	Frequency (%)
50	2	0.1%
42	1	< 0.1%
41	1	< 0.1%
40	3	0.1%
38	2	0.1%
37	1	< 0.1%
36	2	0.1%
35	8	0.4%
34	2	0.1%
33	6	0.3%

age_cat
Categorical

HIGH CORRELATION

Distinct	2
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	17.8 KiB

Under 30	1211
At least 30	1050

Length

Max length	11
Median length	8
Mean length	9.393188854
Min length	8

Characters and Unicode

Total characters	21238
Distinct characters	13
Distinct categories	4 ?
Distinct scripts	2 ?
Distinct blocks	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	At least 30
2nd row	At least 30
3rd row	Under 30
4th row	At least 30
5th row	Under 30

Common Values

Value	Count	Frequency (%)
Under 30	1211	53.6%
At least 30	1050	46.4%

Length

Histogram of lengths of the category

Category Frequency Plot

Value	Count	Frequency (%)
30	2261	40.6%
under	1211	21.7%
at	1050	18.8%
least	1050	18.8%

Most occurring characters

Value	Count	Frequency (%)
	3311	15.6%
e	2261	10.6%
3	2261	10.6%
0	2261	10.6%
t	2100	9.9%
U	1211	5.7%
n	1211	5.7%
d	1211	5.7%
r	1211	5.7%
A	1050	4.9%
Other values (3)	3150	14.8%

Most occurring categories

Value	Count	Frequency (%)
Lowercase Letter	11144	52.5%
Decimal Number	4522	21.3%
Space Separator	3311	15.6%
Uppercase Letter	2261	10.6%

Most frequent character per category

Lowercase Letter

Value	Count	Frequency (%)
e	2261	20.3%
t	2100	18.8%
n	1211	10.9%
d	1211	10.9%
r	1211	10.9%
l	1050	9.4%
a	1050	9.4%
s	1050	9.4%

Decimal Number

Value	Count	Frequency (%)
3	2261	50.0%
0	2261	50.0%

Uppercase Letter

Value	Count	Frequency (%)
U	1211	53.6%
A	1050	46.4%

Space Separator

Value	Count	Frequency (%)
	3311	100.0%

Most occurring scripts

Value	Count	Frequency (%)
Latin	13405	63.1%
Common	7833	36.9%

Most frequent character per script

Latin

Value	Count	Frequency (%)
e	2261	16.9%
t	2100	15.7%
U	1211	9.0%
n	1211	9.0%
d	1211	9.0%
r	1211	9.0%
A	1050	7.8%
l	1050	7.8%
a	1050	7.8%
s	1050	7.8%

Common

Value	Count	Frequency (%)
	3311	42.3%
3	2261	28.9%
0	2261	28.9%

Most occurring blocks

Value	Count	Frequency (%)
ASCII	21238	100.0%

Most frequent character per block

ASCII

Value	Count	Frequency (%)
	3311	15.6%
e	2261	10.6%
3	2261	10.6%
0	2261	10.6%
t	2100	9.9%
U	1211	5.7%
n	1211	5.7%
d	1211	5.7%
r	1211	5.7%
A	1050	4.9%
Other values (3)	3150	14.8%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

First rows

	respondent	main_branch	hobbyist	age	age_1st_code	age_first_code_cut	comp_freq	comp_total	converted_comp	country	currency_desc	currency_symbol	database_desire_next_year	database_worked_with	dev_type	ed_level	employment	ethnicity	gender	job_factors	job_sat	job_seek	language_desire_next_year	language_worked_with	misc_tech_desire_next_year	misc_tech_worked_with	new_collab_tools_desire_next_year	new_collab_tools_worked_with	new_dev_ops	new_dev_ops_impt	new_ed_impt	new_job_hunt	new_job_hunt_research	new_learn	new_off_topic	new_onboard_good	new_other_comms	new_overtime	new_purchase_research	purple_link	newso_sites	new_stuck	op_sys	org_size	platform_desire_next_year	platform_worked_with	purchase_what	sexuality	so_account	so_comm	so_part_freq	so_visit_freq	survey_ease	survey_length	trans	undergrad_major	webframe_desire_next_year	webframe_worked_with	welcome_change	work_week_hrs	years_code	years_code_pro	age_cat
0	36.0	I am not primarily a developer, but I write code sometimes as part of my work	Yes	34.0	30.0	adult	Yearly	60000.0	77556.0	United Kingdom	Pound sterling	GBP	Microsoft SQL Server;MongoDB;SQLite	IBM DB2;Microsoft SQL Server;MongoDB;SQLite	Data or business analyst;Data scientist or machine learning specialist	Some college/university study without earning a degree	Employed full-time	White or of European descent	Man	Flex time or a flexible schedule;Office environment or company culture;Opportunities for professional development	Slightly satisfied	I’m not actively looking, but I am open to new opportunities	C#;Go;HTML/CSS;JavaScript;Python;SQL	C#;Go;HTML/CSS;Java;JavaScript;Python;R;SQL	Keras;Node.js;Pandas;TensorFlow	Node.js;Pandas	Confluence;Jira;Github;Slack;Trello	Confluence;Jira;Github;Slack;Trello	Not sure	Neutral	Somewhat important	Having a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities	None	Every few months	No	Yes	No	Sometimes: 1-2 days per month but less than weekly	None	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	Windows	1,000 to 4,999 employees	Linux;MacOS;Windows	MacOS;Windows	I have little or no influence	Straight / Heterosexual	Yes	Yes, somewhat	Less than once per month or monthly	Multiple times per day	Easy	Appropriate in length	No	Computer science, computer engineering, or software engineering	Express;React.js	Express;React.js	Just as welcome now as I felt last year	40.0	4.0	3.0	At least 30
1	47.0	I am a developer by profession	Yes	53.0	10.0	child	Yearly	58000.0	74970.0	United Kingdom	Pound sterling	GBP	PostgreSQL;SQLite	Microsoft SQL Server;Oracle;PostgreSQL;SQLite	Data scientist or machine learning specialist;Developer, back-end;Developer, QA or test;Engineer, data;Scientist	Other doctoral degree (Ph.D., Ed.D., etc.)	Employed full-time	White or of European descent	Man	Remote work options;How widely used or impactful my work output would be;Opportunities for professional development	Very satisfied	I’m not actively looking, but I am open to new opportunities	Bash/Shell/PowerShell;Java;Python;SQL	Bash/Shell/PowerShell;C#;Java;JavaScript;Python;Ruby;SQL	Pandas	.NET;.NET Core	Github;Gitlab	Confluence;Jira;Github;Gitlab;Microsoft Azure;Google Suite (Docs, Meet, etc)	Yes	Neutral	Not at all important/not necessary	Just because;Having a bad day (or week or month) at work;Curious about other opportunities;Wanting to work with new technologies;Looking to relocate	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company	Once a year	No	Onboarding? What onboarding?	Yes	Occasionally: 1-2 days per quarter but less than monthly	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)	Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later	Linux-based	10 to 19 employees	Arduino;Docker;Linux;Raspberry Pi	Arduino;AWS;Linux;Microsoft Azure;Raspberry Pi	I have some influence	Straight / Heterosexual	Yes	Yes, definitely	A few times per week	A few times per week	Neither easy nor difficult	Appropriate in length	No	A natural science (such as biology, chemistry, physics, etc.)	Flask;Spring	Flask;Spring	Just as welcome now as I felt last year	40.0	43.0	28.0	At least 30
2	69.0	I am a developer by profession	Yes	25.0	12.0	child	Yearly	550000.0	594539.0	France	European Euro	EUR	PostgreSQL	MongoDB	Data scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, full-stack;Engineer, data	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	White or of European descent	Man	Flex time or a flexible schedule;How widely used or impactful my work output would be;Opportunities for professional development	Very satisfied	I am not interested in new job opportunities	Python;Rust;Scala;SQL	HTML/CSS;Python	Keras;Pandas;TensorFlow	Keras;Pandas;TensorFlow	Github;Slack;Google Suite (Docs, Meet, etc)	Confluence;Jira;Github;Slack;Google Suite (Docs, Meet, etc)	Yes	Extremely important	Very important	Curious about other opportunities;Better compensation;Trouble with leadership at my company;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate	Read company media, such as employee blogs or company culture videos;Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	Once a year	No	No	No	Sometimes: 1-2 days per month but less than weekly	Ask developers I know/work with;Visit developer communities like Stack Overflow	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	MacOS	20 to 99 employees	Kubernetes;Linux	Linux;Microsoft Azure	I have some influence	Bisexual	Yes	Yes, somewhat	A few times per month or weekly	A few times per week	Easy	Too short	No	Computer science, computer engineering, or software engineering	Django;Flask	Django;Flask	Just as welcome now as I felt last year	40.0	13.0	3.0	Under 30
3	125.0	I am not primarily a developer, but I write code sometimes as part of my work	Yes	41.0	30.0	adult	Monthly	200000.0	2000000.0	United States	United States dollar	USD	PostgreSQL	PostgreSQL	Data scientist or machine learning specialist;Scientist	Other doctoral degree (Ph.D., Ed.D., etc.)	Employed full-time	White or of European descent	Man	Flex time or a flexible schedule;Family friendliness	Very satisfied	I am not interested in new job opportunities	Python	Python;SQL	Keras;Pandas;TensorFlow;Torch/PyTorch	Keras;Pandas;TensorFlow	Jira	Jira	Not sure	Neutral	Critically important	Better work/life balance	Personal network - friends or family;Directly asking current or past employees at the company	Once every few years	No	Yes	No	Occasionally: 1-2 days per quarter but less than monthly	None	Amused	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Visit another developer community (please name):	Windows	5,000 to 9,999 employees	Docker;Kubernetes	Docker	I have little or no influence	Straight / Heterosexual	Not sure/can't remember	No, not really	None	Multiple times per day	Easy	Appropriate in length	No	None	None	None	Just as welcome now as I felt last year	40.0	11.0	11.0	At least 30
4	147.0	I am not primarily a developer, but I write code sometimes as part of my work	No	28.0	15.0	adult	Yearly	50000.0	37816.0	Canada	Canadian dollar	CAD	None	MariaDB	Data scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, front-end;Developer, full-stack	Bachelor’s degree (B.A., B.S., B.Eng., etc.)	Employed full-time	White or of European descent	Man	Diversity of the company or organization;Flex time or a flexible schedule;Office environment or company culture	Very satisfied	I am not interested in new job opportunities	Rust	HTML/CSS;JavaScript;Python;TypeScript	None	Node.js;Pandas	None	Github;Slack	No	Somewhat important	Very important	Curious about other opportunities;Wanting to work with new technologies;Growth or leadership opportunities	Read company media, such as employee blogs or company culture videos;Personal network - friends or family	Once a year	No	Onboarding? What onboarding?	Yes	Rarely: 1-2 days per year or less	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Play games;Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Visit another developer community (please name):	Windows	2 to 9 employees	Docker;Kubernetes	None	I have some influence	Straight / Heterosexual	Yes	Yes, somewhat	A few times per month or weekly	Daily or almost daily	Easy	Appropriate in length	No	Another engineering discipline (such as civil, electrical, mechanical, etc.)	None	Express;Flask	Just as welcome now as I felt last year	40.0	5.0	3.0	Under 30
5	152.0	I am a developer by profession	Yes	30.0	16.0	adult	Monthly	10000.0	121980.0	Switzerland	Swiss franc	CHF	Redis	Redis	Data scientist or machine learning specialist;Developer, back-end;Developer, front-end;Developer, full-stack	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	White or of European descent	Man	Languages, frameworks, and other technologies I’d be working with;How widely used or impactful my work output would be;Office environment or company culture	Very satisfied	I’m not actively looking, but I am open to new opportunities	Bash/Shell/PowerShell;C#	Bash/Shell/PowerShell;C#;HTML/CSS;JavaScript;Python;R	.NET Core	.NET;.NET Core	Gitlab	Github;Gitlab	No	Extremely important	Fairly important	None	Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	Once a year	No	Onboarding? What onboarding?	No	Sometimes: 1-2 days per month but less than weekly	Start a free trial;Ask developers I know/work with	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Visit Stack Overflow;Do other work and come back later	Windows	2 to 9 employees	Docker;Kubernetes;Linux;Raspberry Pi;Windows	Docker;Kubernetes;Linux;Raspberry Pi;Windows	I have a great deal of influence	Straight / Heterosexual	Yes	No, not really	Less than once per month or monthly	Daily or almost daily	Easy	Appropriate in length	No	Mathematics or statistics	ASP.NET Core	ASP.NET;ASP.NET Core;jQuery;Vue.js	Just as welcome now as I felt last year	40.0	14.0	6.0	Under 30
6	166.0	I am a developer by profession	Yes	28.0	9.0	child	Yearly	45000.0	48644.0	Germany	European Euro	EUR	PostgreSQL	Elasticsearch;MongoDB;Redis	Academic researcher;Data or business analyst;Data scientist or machine learning specialist;Database administrator;Engineer, data;Scientist	Other doctoral degree (Ph.D., Ed.D., etc.)	Employed full-time	White or of European descent	Man	Specific department or team I’d be working on;Financial performance or funding status of the company or organization;Opportunities for professional development	Neither	I am actively looking for a job	Bash/Shell/PowerShell;C++;Python;SQL	Bash/Shell/PowerShell;Python	Pandas;TensorFlow;Teraform;Torch/PyTorch	Pandas	Github;Gitlab	Jira;Github;Slack	Not sure	Somewhat important	Fairly important	Having a bad day (or week or month) at work;Wanting to share accomplishments with a wider network;Curious about other opportunities;Better compensation;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase)	Once every few years	Not sure	Yes	Yes	Sometimes: 1-2 days per month but less than weekly	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)	Visit Stack Overflow;Panic	Linux-based	2 to 9 employees	AWS;Google Cloud Platform;Linux;Raspberry Pi	AWS;Docker;Linux	I have a great deal of influence	Straight / Heterosexual	Yes	Yes, somewhat	Less than once per month or monthly	Multiple times per day	Neither easy nor difficult	Appropriate in length	No	A natural science (such as biology, chemistry, physics, etc.)	None	None	Just as welcome now as I felt last year	40.0	15.0	6.0	Under 30
7	170.0	I am a developer by profession	Yes	26.0	17.0	adult	Yearly	130000.0	130000.0	United States	United States dollar	USD	MySQL;PostgreSQL;Redis	Elasticsearch;Firebase;MySQL;PostgreSQL;SQLite	Data scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, front-end;Developer, full-stack;Developer, game or graphics;Developer, mobile;Educator;Engineer, site reliability;Engineering manager;System administrator	Bachelor’s degree (B.A., B.S., B.Eng., etc.)	Employed full-time	White or of European descent	Man	Languages, frameworks, and other technologies I’d be working with;Office environment or company culture;Opportunities for professional development	Very satisfied	I am not interested in new job opportunities	C#;HTML/CSS;JavaScript;PHP;Python;SQL;TypeScript	C#;HTML/CSS;JavaScript;PHP;Python;SQL	.NET;Keras;Node.js;Pandas;React Native;TensorFlow;Torch/PyTorch;Unity 3D;Xamarin	.NET;Keras;Node.js;Pandas;React Native;TensorFlow;Unity 3D	Jira;Github;Gitlab;Microsoft Azure	Github;Gitlab;Facebook Workplace	Yes	Extremely important	Not at all important/not necessary	Having a bad day (or week or month) at work;Wanting to share accomplishments with a wider network;Curious about other opportunities;Better compensation;Trouble with leadership at my company;Better work/life balance;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Read other media like news articles, founder profiles, etc. about the company	Every few months	No	Yes	No	Sometimes: 1-2 days per month but less than weekly	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)	Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	Linux-based	20 to 99 employees	Android;Arduino;AWS;Docker;Kubernetes;Linux;Raspberry Pi;Windows	Android;Arduino;AWS;Docker;Heroku;Linux;Windows	I have a great deal of influence	Straight / Heterosexual	Yes	No, not really	I have never participated in Q&A on Stack Overflow	Daily or almost daily	Easy	Appropriate in length	No	A humanities discipline (such as literature, history, philosophy, etc.)	Django;Express;Flask;jQuery;Laravel;React.js	Django;Express;Flask;jQuery;React.js	Just as welcome now as I felt last year	45.0	4.0	2.0	Under 30
8	187.0	I am a developer by profession	Yes	43.0	12.0	child	Yearly	125000.0	94539.0	Canada	Canadian dollar	CAD	MongoDB;PostgreSQL	Microsoft SQL Server;MongoDB	Data scientist or machine learning specialist;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, full-stack	Other doctoral degree (Ph.D., Ed.D., etc.)	Employed full-time	White or of European descent	Man	Industry that I’d be working in;Languages, frameworks, and other technologies I’d be working with;Office environment or company culture	Slightly satisfied	I’m not actively looking, but I am open to new opportunities	Julia	Python;SQL	Pandas	Pandas	Confluence;Jira;Github;Microsoft Teams	Confluence;Jira;Github;Microsoft Teams	Yes	Extremely important	Fairly important	Curious about other opportunities;Better compensation;Growth or leadership opportunities	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind)	Once a year	Not sure	Yes	No	Rarely: 1-2 days per year or less	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers)	Call a coworker or friend;Visit Stack Overflow	Windows	1,000 to 4,999 employees	Google Cloud Platform;Windows	AWS;Linux;Windows	I have some influence	Straight / Heterosexual	Yes	Neutral	Less than once per month or monthly	Daily or almost daily	Easy	Appropriate in length	No	A natural science (such as biology, chemistry, physics, etc.)	Django;Flask	Django;Flask	A lot less welcome now than last year	37.5	30.0	18.0	At least 30
9	196.0	I am a developer by profession	Yes	23.0	15.0	adult	Monthly	160000.0	1920000.0	United States	United States dollar	USD	DynamoDB;Elasticsearch;MongoDB;MySQL;Oracle;PostgreSQL;Redis	Cassandra;Couchbase;Redis;SQLite	Academic researcher;Data scientist or machine learning specialist;Designer;Developer, back-end;Developer, front-end;Developer, full-stack;DevOps specialist	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	South Asian	Man	Languages, frameworks, and other technologies I’d be working with;Specific department or team I’d be working on;Opportunities for professional development	Very satisfied	I’m not actively looking, but I am open to new opportunities	Go;HTML/CSS;Java;JavaScript;PHP;Python;R;SQL	Assembly;Bash/Shell/PowerShell;C;C++;Scala	Apache Spark;Hadoop;Keras;Node.js;Pandas;TensorFlow;Teraform;Torch/PyTorch	None	Github;Gitlab;Microsoft Azure;Google Suite (Docs, Meet, etc)	Jira	No	Neutral	Critically important	Curious about other opportunities;Better compensation;Growth or leadership opportunities	Personal network - friends or family;Directly asking current or past employees at the company	Every few months	No	Yes	No	Never	Ask developers I know/work with;Visit developer communities like Stack Overflow	Annoyed	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Visit Stack Overflow;Do other work and come back later	MacOS	10,000 or more employees	AWS;Docker;Google Cloud Platform;Heroku;Kubernetes;Linux;MacOS;Microsoft Azure;WordPress	Android;Windows	I have some influence	Straight / Heterosexual	Yes	Yes, definitely	Multiple times per day	Multiple times per day	Easy	Too long	No	Computer science, computer engineering, or software engineering	Flask;jQuery;Laravel;React.js	Angular;Angular.js;ASP.NET;ASP.NET Core	Just as welcome now as I felt last year	40.0	8.0	3.0	Under 30

Last rows

	respondent	main_branch	hobbyist	age	age_1st_code	age_first_code_cut	comp_freq	comp_total	converted_comp	country	currency_desc	currency_symbol	database_desire_next_year	database_worked_with	dev_type	ed_level	employment	ethnicity	gender	job_factors	job_sat	job_seek	language_desire_next_year	language_worked_with	misc_tech_desire_next_year	misc_tech_worked_with	new_collab_tools_desire_next_year	new_collab_tools_worked_with	new_dev_ops	new_dev_ops_impt	new_ed_impt	new_job_hunt	new_job_hunt_research	new_learn	new_off_topic	new_onboard_good	new_other_comms	new_overtime	new_purchase_research	purple_link	newso_sites	new_stuck	op_sys	org_size	platform_desire_next_year	platform_worked_with	purchase_what	sexuality	so_account	so_comm	so_part_freq	so_visit_freq	survey_ease	survey_length	trans	undergrad_major	webframe_desire_next_year	webframe_worked_with	welcome_change	work_week_hrs	years_code	years_code_pro	age_cat
2251	62608.0	I am a developer by profession	Yes	30.0	19.0	adult	Yearly	155000.0	155000.0	United States	United States dollar	USD	PostgreSQL	Cassandra;Elasticsearch;MongoDB;PostgreSQL;Redis	Data scientist or machine learning specialist;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, embedded applications or devices;Developer, front-end;Developer, full-stack;Developer, game or graphics;Developer, mobile;Developer, QA or test;DevOps specialist;Engineer, data;Engineering manager;Product manager;Scientist;Senior executive/VP;System administrator	Some college/university study without earning a degree	Employed full-time	White or of European descent	Man	Diversity of the company or organization;Flex time or a flexible schedule;Remote work options	Very satisfied	I am not interested in new job opportunities	Rust;SQL;Swift;TypeScript	Bash/Shell/PowerShell;C;C++;Go;HTML/CSS;JavaScript;Python;Rust;SQL;Swift;TypeScript	Keras;TensorFlow;Teraform	Keras;Node.js;TensorFlow;Teraform;Unity 3D;Unreal Engine	Github;Gitlab;Slack	Github;Gitlab;Slack	No	Neutral	Somewhat important	Curious about other opportunities;Better compensation;Better work/life balance	Read company media, such as employee blogs or company culture videos;Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Directly asking current or past employees at the company	None	No	Yes	Yes	Often: 1-2 days per week or more	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Research companies that have advertised on sites I visit	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Meditate;Go for a walk or other physical activity	MacOS	20 to 99 employees	Docker;Google Cloud Platform;iOS;Kubernetes;Linux;MacOS	AWS;Docker;Google Cloud Platform;iOS;Kubernetes;Linux;MacOS	I have a great deal of influence	Straight / Heterosexual	Yes	Yes, somewhat	Less than once per month or monthly	A few times per month or weekly	Easy	Appropriate in length	No	Computer science, computer engineering, or software engineering	None	Express;React.js;Vue.js	Just as welcome now as I felt last year	40.0	10.0	10.0	Under 30
2252	62616.0	I am a developer by profession	Yes	30.0	19.0	adult	Yearly	1560000.0	1560000.0	United States	United States dollar	USD	PostgreSQL	Microsoft SQL Server;Oracle;PostgreSQL	Data scientist or machine learning specialist	Bachelor’s degree (B.A., B.S., B.Eng., etc.)	Employed full-time	White or of European descent	Man	Flex time or a flexible schedule;How widely used or impactful my work output would be;Office environment or company culture	Neither	I’m not actively looking, but I am open to new opportunities	C++;Julia;Python;Rust;SQL	C;C++;Python;Rust;SQL	Apache Spark;Hadoop;TensorFlow;Torch/PyTorch	Apache Spark;Hadoop;TensorFlow;Torch/PyTorch	Github;Slack	Confluence;Github;Slack	Yes	Extremely important	Very important	Having a bad day (or week or month) at work;Curious about other opportunities;Trouble with leadership at my company;Wanting to work with new technologies;Growth or leadership opportunities	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Directly asking current or past employees at the company	Once a year	Not sure	Onboarding? What onboarding?	Yes	Occasionally: 1-2 days per quarter but less than monthly	None	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Meditate;Do other work and come back later	MacOS	10,000 or more employees	AWS;Docker;Google Cloud Platform;Linux;Microsoft Azure;Raspberry Pi	Docker;Linux;Microsoft Azure;Raspberry Pi;Windows	I have little or no influence	Straight / Heterosexual	No	Neutral	None	Daily or almost daily	Easy	Appropriate in length	No	Mathematics or statistics	None	None	Just as welcome now as I felt last year	45.0	11.0	5.0	Under 30
2253	62617.0	I am a developer by profession	Yes	26.0	14.0	adult	Monthly	10000.0	30372.0	Poland	Polish zloty	PLN	None	Elasticsearch;Redis	Academic researcher;Data scientist or machine learning specialist;Developer, game or graphics;Scientist	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	White or of European descent	Man	Specific department or team I’d be working on;Office environment or company culture;Opportunities for professional development	Very satisfied	I’m not actively looking, but I am open to new opportunities	Python;Rust	C++;Python;Rust;TypeScript	TensorFlow;Torch/PyTorch	Pandas;TensorFlow;Torch/PyTorch	Github;Gitlab;Microsoft Teams	Confluence;Github;Gitlab;Slack;Microsoft Teams	Yes	Somewhat important	Very important	Wanting to share accomplishments with a wider network;Curious about other opportunities;Better compensation;Wanting to work with new technologies	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	Once a year	Yes	Yes	Yes	Rarely: 1-2 days per year or less	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Research companies that have advertised on sites I visit	Indifferent	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Call a coworker or friend;Visit Stack Overflow;Watch help / tutorial videos;Do other work and come back later	Linux-based	20 to 99 employees	Docker;Linux	Android;Docker;Kubernetes;Linux;Windows	I have a great deal of influence	Straight / Heterosexual	Yes	Neutral	A few times per month or weekly	Daily or almost daily	Neither easy nor difficult	Too long	No	Computer science, computer engineering, or software engineering	React.js	Angular;Flask;React.js;Vue.js	Just as welcome now as I felt last year	40.0	10.0	5.0	Under 30
2254	62705.0	I am a developer by profession	Yes	24.0	18.0	adult	Yearly	1800000.0	25131.0	India	Indian rupee	INR	MongoDB;MySQL;PostgreSQL;Redis;SQLite	MongoDB;MySQL;PostgreSQL;Redis;SQLite	Academic researcher;Data or business analyst;Data scientist or machine learning specialist;Database administrator;Developer, back-end;Developer, desktop or enterprise applications;Developer, full-stack;Educator;Engineer, data;Engineer, site reliability;Scientist;System administrator	Bachelor’s degree (B.A., B.S., B.Eng., etc.)	Employed full-time	East Asian	Man	Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Opportunities for professional development	Slightly dissatisfied	I’m not actively looking, but I am open to new opportunities	Assembly;Bash/Shell/PowerShell;C#;C++;Go;HTML/CSS;Java;JavaScript;Kotlin;Perl;Python;R;Ruby;Rust;SQL;TypeScript	Assembly;Bash/Shell/PowerShell;C;C++;HTML/CSS;Java;JavaScript;Python;R;Ruby;Rust;SQL	Ansible;Apache Spark;Chef;Cordova;Hadoop;Keras;Node.js;Pandas;Puppet;React Native;TensorFlow;Teraform;Torch/PyTorch	Node.js;Pandas;Puppet;TensorFlow;Torch/PyTorch	Confluence;Jira;Github;Slack;Trello;Google Suite (Docs, Meet, etc)	Confluence;Jira;Github;Slack;Trello;Google Suite (Docs, Meet, etc)	No	Extremely important	Very important	Curious about other opportunities;Better compensation;Wanting to work with new technologies;Growth or leadership opportunities	Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	Every few months	Yes	Yes	Yes	Rarely: 1-2 days per year or less	None	Amused	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations);Stack Overflow Talent (for hiring companies/recruiters);Stack Overflow Advertising (for technology companies)	Visit Stack Overflow;Panic;Watch help / tutorial videos;Do other work and come back later	MacOS	10,000 or more employees	AWS;Docker;Kubernetes;Linux;MacOS;Raspberry Pi;Windows	AWS;Docker;Kubernetes;Linux;MacOS;Raspberry Pi;Windows	I have little or no influence	Straight / Heterosexual	Yes	No, not at all	Less than once per month or monthly	Multiple times per day	Neither easy nor difficult	Appropriate in length	No	Computer science, computer engineering, or software engineering	Angular;Angular.js;Django;Express;Flask;jQuery;Laravel;React.js;Ruby on Rails;Spring;Vue.js	Django;Flask;jQuery;Spring	Just as welcome now as I felt last year	40.0	7.0	3.0	Under 30
2255	62745.0	I am a developer by profession	Yes	24.0	14.0	adult	Yearly	50000.0	64630.0	United Kingdom	Pound sterling	GBP	Elasticsearch;PostgreSQL;SQLite	MongoDB;PostgreSQL;SQLite	Data scientist or machine learning specialist;Developer, full-stack	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	White or of European descent	Man	Industry that I’d be working in;Languages, frameworks, and other technologies I’d be working with;Remote work options	Slightly satisfied	I’m not actively looking, but I am open to new opportunities	Bash/Shell/PowerShell;C++;Go;HTML/CSS;Python;Rust	Bash/Shell/PowerShell;Go;HTML/CSS;JavaScript;Python;Rust;SQL;TypeScript	Pandas	Node.js;Pandas	Github;Google Suite (Docs, Meet, etc)	Jira;Github;Gitlab;Slack;Microsoft Azure;Trello;Google Suite (Docs, Meet, etc)	No	Neutral	Fairly important	Curious about other opportunities;Better compensation;Trouble with my teammates;Wanting to work with new technologies;Growth or leadership opportunities;Looking to relocate	Read company media, such as employee blogs or company culture videos;Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	Every few months	No	No	Yes	Never	Ask developers I know/work with	Annoyed	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Visit another developer community (please name):	Linux-based	20 to 99 employees	Docker;Linux;MacOS	Docker;Google Cloud Platform;Linux;MacOS;Microsoft Azure	I have a great deal of influence	Straight / Heterosexual	Yes	No, not really	Less than once per month or monthly	Multiple times per day	Easy	Too short	No	Computer science, computer engineering, or software engineering	Flask;Vue.js	Angular;Flask;jQuery;React.js	Somewhat more welcome now than last year	30.0	8.0	2.0	Under 30
2256	62812.0	I am a developer by profession	Yes	40.0	10.0	child	Yearly	145000.0	145000.0	United States	United States dollar	USD	Cassandra;Elasticsearch;MariaDB;Microsoft SQL Server;MongoDB;MySQL;Oracle;PostgreSQL;Redis	Cassandra;MariaDB;Microsoft SQL Server;MongoDB;MySQL;Oracle;PostgreSQL;Redis;SQLite	Data or business analyst;Data scientist or machine learning specialist;Database administrator;Designer;Developer, back-end;Developer, desktop or enterprise applications;Developer, front-end;Developer, full-stack;DevOps specialist;Educator;Engineer, data;Engineering manager;System administrator	Associate degree (A.A., A.S., etc.)	Employed full-time	White or of European descent	Man	Flex time or a flexible schedule;Languages, frameworks, and other technologies I’d be working with;Financial performance or funding status of the company or organization	Very satisfied	I am not interested in new job opportunities	Bash/Shell/PowerShell;HTML/CSS;JavaScript;Perl;Python;SQL	Bash/Shell/PowerShell;Go;HTML/CSS;JavaScript;Perl;PHP;Python;R;SQL	Ansible;Apache Spark;Chef;Hadoop;Keras;Pandas;TensorFlow;Teraform;Torch/PyTorch	Ansible;Apache Spark;Chef;Hadoop;Keras;Node.js;Pandas;Puppet;TensorFlow;Teraform;Torch/PyTorch	Confluence;Jira;Github;Gitlab;Slack;Microsoft Teams;Microsoft Azure;Google Suite (Docs, Meet, etc)	Confluence;Jira;Github;Gitlab;Google Suite (Docs, Meet, etc)	Yes	Extremely important	Fairly important	Better compensation;Trouble with my direct manager;Better work/life balance;Growth or leadership opportunities	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	Once every few years	No	No	Yes	Sometimes: 1-2 days per month but less than weekly	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Research companies that have advertised on sites I visit;Research companies that have emailed me	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Meditate;Visit Stack Overflow;Go for a walk or other physical activity;Watch help / tutorial videos;Do other work and come back later	Linux-based	10,000 or more employees	Android;AWS;Docker;Google Cloud Platform;IBM Cloud or Watson;Kubernetes;Linux;Microsoft Azure;WordPress	Android;AWS;Docker;Google Cloud Platform;IBM Cloud or Watson;Kubernetes;Linux;WordPress	I have a great deal of influence	Straight / Heterosexual	Yes	No, not really	Less than once per month or monthly	A few times per month or weekly	Easy	Too long	No	Computer science, computer engineering, or software engineering	Flask;jQuery	Angular;Angular.js;Flask;jQuery;React.js	Somewhat less welcome now than last year	50.0	30.0	20.0	At least 30
2257	62835.0	I am a developer by profession	Yes	23.0	9.0	child	Monthly	180000.0	33972.0	Russian Federation	Russian ruble	RUB	Cassandra;PostgreSQL;Redis	PostgreSQL;Redis	Data scientist or machine learning specialist;Developer, back-end;Engineering manager	Bachelor’s degree (B.A., B.S., B.Eng., etc.)	Employed full-time	White or of European descent	Man	Industry that I’d be working in;Specific department or team I’d be working on;Opportunities for professional development	Very satisfied	I am not interested in new job opportunities	C#;Haskell;Rust;Scala;SQL	Bash/Shell/PowerShell;C;C#;C++;Go;Haskell;Kotlin;Python;Scala;SQL	.NET Core;Apache Spark;Keras;TensorFlow;Torch/PyTorch	.NET Core;Keras;TensorFlow	Github;Gitlab;Google Suite (Docs, Meet, etc)	Confluence;Jira;Github;Gitlab;Slack	Yes	Extremely important	Somewhat important	Curious about other opportunities	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	Every few months	No	Yes	Yes	Often: 1-2 days per week or more	Start a free trial;Ask developers I know/work with;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)	Call a coworker or friend;Visit Stack Overflow	MacOS	100 to 499 employees	Docker;Kubernetes	Docker;Kubernetes;Slack Apps and Integrations	I have a great deal of influence	Straight / Heterosexual	Yes	Yes, somewhat	I have never participated in Q&A on Stack Overflow	A few times per month or weekly	Easy	Too short	No	Computer science, computer engineering, or software engineering	ASP.NET Core	ASP.NET Core;Flask	Just as welcome now as I felt last year	60.0	8.0	3.0	Under 30
2258	62837.0	I am a developer by profession	Yes	27.0	8.0	child	Monthly	7500.0	97284.0	Germany	European Euro	EUR	None	None	Data scientist or machine learning specialist;Developer, back-end	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	White or of European descent	Man	Languages, frameworks, and other technologies I’d be working with;Specific department or team I’d be working on;Office environment or company culture	Very satisfied	I’m not actively looking, but I am open to new opportunities	Assembly;Haskell;Rust	C++;Python;Rust	Torch/PyTorch	None	Github;Google Suite (Docs, Meet, etc)	Jira;Github;Slack;Google Suite (Docs, Meet, etc);Stack Overflow for Teams	Yes	Somewhat important	Somewhat important	None	Read company media, such as employee blogs or company culture videos;Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company	Every few months	No	Yes	None	Sometimes: 1-2 days per month but less than weekly	None	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow Jobs (for job seekers);Stack Overflow for Teams (private Q&A for organizations)	Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Do other work and come back later	Linux-based	500 to 999 employees	AWS;Docker;Kubernetes;Linux	AWS;Docker;Linux	I have little or no influence	Straight / Heterosexual	Yes	Yes, somewhat	Less than once per month or monthly	Daily or almost daily	Easy	Appropriate in length	No	Mathematics or statistics	None	None	Just as welcome now as I felt last year	42.0	12.0	2.0	Under 30
2259	62867.0	I am not primarily a developer, but I write code sometimes as part of my work	Yes	33.0	13.0	child	Monthly	6000.0	72000.0	Panama	United States dollar	USD	None	MariaDB;Microsoft SQL Server;MongoDB;SQLite	Data or business analyst;Data scientist or machine learning specialist;Engineering manager	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	Hispanic or Latino/a/x	Man	How widely used or impactful my work output would be;Office environment or company culture;Opportunities for professional development	Very satisfied	I’m not actively looking, but I am open to new opportunities	Python;R;Ruby	JavaScript;Python;SQL	React Native;TensorFlow	Node.js;Pandas	Jira;Gitlab;Slack	Github;Microsoft Teams;Trello	Not sure	Extremely important	Somewhat important	Curious about other opportunities;Better compensation	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind);Publicly available financial information (e.g. Crunchbase);Personal network - friends or family;Read other media like news articles, founder profiles, etc. about the company;Directly asking current or past employees at the company	Once every few years	Not sure	Yes	Yes	Often: 1-2 days per week or more	Start a free trial;Ask developers I know/work with;Research companies that have emailed me	Hello, old friend	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics)	Meditate;Play games;Call a coworker or friend;Visit Stack Overflow;Go for a walk or other physical activity;Panic;Watch help / tutorial videos;Do other work and come back later;Visit another developer community (please name):	Windows	10,000 or more employees	None	AWS;Google Cloud Platform;Windows	I have some influence	Straight / Heterosexual	Yes	Neutral	I have never participated in Q&A on Stack Overflow	Daily or almost daily	Neither easy nor difficult	Too long	No	Another engineering discipline (such as civil, electrical, mechanical, etc.)	None	Django;React.js	A lot less welcome now than last year	45.0	15.0	2.0	At least 30
2260	62882.0	I am a developer by profession	Yes	28.0	13.0	child	Yearly	180000.0	180000.0	United States	United States dollar	USD	Cassandra;PostgreSQL;Redis	DynamoDB;Elasticsearch;MariaDB;MongoDB;MySQL;Oracle;PostgreSQL;Redis;SQLite	Data scientist or machine learning specialist;Developer, back-end;Developer, front-end;Developer, full-stack	Master’s degree (M.A., M.S., M.Eng., MBA, etc.)	Employed full-time	South Asian	Man	Languages, frameworks, and other technologies I’d be working with;Specific department or team I’d be working on;Financial performance or funding status of the company or organization	Very satisfied	I am not interested in new job opportunities	JavaScript;Python;Scala;TypeScript	Bash/Shell/PowerShell;C;C++;JavaScript;Python	Apache Spark;Hadoop;Node.js;Pandas;Torch/PyTorch	None	Confluence;Github;Slack;Stack Overflow for Teams	Confluence;Jira;Github;Slack;Microsoft Teams;Stack Overflow for Teams	No	Extremely important	Critically important	Better compensation	Read company media, such as employee blogs or company culture videos;Company reviews from third party sites (e.g. Glassdoor, Blind)	Every few months	None	Yes	No	Rarely: 1-2 days per year or less	Start a free trial;Visit developer communities like Stack Overflow;Read ratings or reviews on third party sites like G2Crowd;Research companies that have advertised on sites I visit	Annoyed	Stack Overflow (public Q&A for anyone who codes);Stack Exchange (public Q&A for a variety of topics);Stack Overflow for Teams (private Q&A for organizations)	Visit Stack Overflow;Watch help / tutorial videos	MacOS	10,000 or more employees	Docker;Heroku;Kubernetes;MacOS;Microsoft Azure;Slack Apps and Integrations	Android;AWS;Docker;Heroku;IBM Cloud or Watson;Linux;MacOS;Slack Apps and Integrations;Windows;WordPress	I have some influence	Straight / Heterosexual	Yes	Yes, somewhat	A few times per month or weekly	Daily or almost daily	Easy	Appropriate in length	No	Computer science, computer engineering, or software engineering	Angular;Express;Flask;React.js	Angular;Angular.js;Django;Drupal;Express;Flask	Just as welcome now as I felt last year	40.0	11.0	5.0	Under 30

Overview

Variables

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Space Separator

Uppercase Letter

Other Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Category Frequency Plot

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Most occurring scripts

Most frequent character per script

Latin

Most occurring blocks

Most frequent character per block

ASCII

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Other Punctuation

Space Separator

Open Punctuation

Close Punctuation

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII

None

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Lowercase Letter

Uppercase Letter

Space Separator

Most occurring scripts

Most frequent character per script

Latin

Common

Most occurring blocks

Most frequent character per block

ASCII